One of the key aspects of human cognition is the ability to interact and react in a visual environment. When we play tennis, we can predict how the ball moves and where it is supposed to hit the ground so we move the tennis racket accordingly. Or consider the scenario in which someone tosses the car keys in your direction and you quickly reposition your hands to catch them. These capabilities in humans start to develop during infancy and they are at the core of the cognition system [3, 8].
Visual reaction requires predicting the future followed by planning accordingly. The future prediction problem has received a lot of attention in the computer vision community. The work in this domain can be divided into two major categories. The first category considers predicting future actions of people or trajectories of cars (e.g.,[5, 22, 25, 58]). Typically, there are multiple correct solutions in these scenarios, and the outcome depends on the intention of the people. The second category is future prediction based on the physics of the scene (e.g., [27, 32, 60, 66]). The works in this category are mostly limited to learning from passive observation of images and videos, and there is no interaction or feedback involved during the prediction process.
In this paper, we tackle the problem of visual reaction: the task of predicting the future movements of objects in a dynamic environment and planning accordingly. The interaction enables us to make decisions on the fly and receive feedback from the environment to update our belief about the future movements. This is in contrast to passive approaches that perform prediction given pre-recorded images or videos. We study this problem in the context of playing catch with a drone, where the goal is to catch a thrown object using only visual ego-centric observations (Figure 1). Compared to the previous approaches, we not only need to predict future movements of the objects, but also to infer a minimal set of actions for the drone to catch the object in a timely manner.
This problem exhibits various challenges. First, objects have different weights, shapes and materials, which makes their trajectories very different. Second, the trajectories vary based on the magnitude and angle of the force used for throwing. Third, the objects might collide with the wall or other structures in the scene, and suddenly change their trajectory. Fourth, the drone movements are not deterministic so the same action might result in different movements. Finally, the agent has limited time to reason and react to the dynamically evolving scene to catch the object before it hits the ground.
Our proposed solution is an adaptation of the model-based Reinforcement Learning paradigm. More specifically, we propose a forecasting network that rolls out the future trajectory of the thrown object from visual observation. We integrate the forecasting network with a model-based planner to estimate the best sequence of drone actions for catching the object. The planner is able to roll out sequences of actions for the drone using the dynamics model and an action sampler to select the best action at each time step. In other words, we learn a policy using the rollout of both object and agent movements.
We perform our experiments in AI2-THOR , a near-photo-realistic interactive environment which models physics of objects and scenes (object weights, friction, collision, etc). Our experiments show that the proposed model outperforms baselines that are based on tracking (current state estimation as opposed to forecasting) and also pure model-free and model-based baselines. We provide an ablation study of our model and show how the performance varies with the number of rollouts and also the length of the planning horizon. Furthermore, we show how the model performs for object categories unseen during training.
The contributions of the paper are as follows: (1) We investigate the problem of visual reaction in an interactive, dynamic, and visually rich environment. (2) We propose a new framework and dataset for visual reaction in the context of playing catch with a drone. (3) We propose a solution by integrating a planner and a forecaster and show it significantly outperforms a number of strong baselines (4) We provide various analyses to better evaluate the models.
2 Related Work
Future prediction & Forecasting. Various works explore future prediction and forecasting from visual data. Several authors consider the problem of predicting the future trajectories of objects from individual [31, 37, 55, 56, 57, 65] and multiple sequential [1, 22, 62] images. Unlike these works, we control an agent that interacts with the environment which causes its observation and viewpoint to change over time. A number of approaches explore prediction from ego-centric views.  predict a plausible set of ego-motion trajectories.  propose an Inverse Reinforcement Learning approach to predict the behavior of a person wearing a camera.  learn visual representation from unlabelled video and use the representation for forecasting objects that appear in an ego-centric video.  predict the future trajectories of interacting objects in a driving scenario. Our agent also forecasts the future trajectory based on ego-centric views of objects, but the prediction is based on physical laws (as opposed to peoples intentions). The problem of predicting future actions or the 3D pose of humans has been explored by [6, 14, 25, 48]. Also, [5, 28, 46, 52, 53, 63] propose methods for generating future frames. Our task is different from the mentioned approaches as they use pre-recorded videos or images during training and inference, while we have an interactive setting. Methods such as  and  consider future prediction in interactive settings. However,  is based on a static third-person camera and  predicts the effect of agent actions and does not consider the physics of the scene.
Planning. There is a large body of work (e.g., [7, 16, 18, 19, 34, 38, 45, 51, 59]) that involves a model-based planner. Our approach is similar to these approaches as we integrate the forecaster with a model-based planner. The work of  shares similarities with our approach. The authors propose learning a compact latent state-space model of the environment and its dynamics; from this model an Imagination-Augmented Agent  learns to produce informative rollouts in the latent space which improve its policy. We instead consider visually complex scenarios in 3D so learning a compact generative model is not as straightforward. Also,  adopts a model-based planner for the task of vision and language navigation. They roll out the future states of the agent to form a model ensemble with model-free RL. Our task is quite different. Moreover, we consider the rollouts for both the agent and the moving object, which makes the problem more challenging.
Object catching in robotics. The problem of catching objects has been studied in the robotics community. Quadrocopters have been used for juggling a ball , throwing and catching a ball , playing table tennis , and catching a flying ball .  consider the problem of catching in-flight objects with uneven shapes. These approaches have one or multiple of the following issues: they use multiple external cameras and landmarks to localize the ball, bypass the vision problem by attaching a distinctive marker to the ball, use the same environment for training and testing, or assume a stationary agent. We acknowledge that experiments on real robots involve complexities such as dealing with air resistance and mechanical constraints that are less accurately modeled in our setting.
There are various works that address the problem of visual navigation towards a static target using deep reinforcement learning or imitation learning (e.g.,[17, 29, 43, 64, 67]). Our problem can be considered as an extension of these works since our target is moving and our agent has a limited amount of time to reach the target. Our work is also different from drone navigation (e.g., [15, 41]) since we tackle the visual reaction problem.
Object tracking. Our approach is different from object tracking (e.g., [2, 9, 11, 35, 49]) as we forecast the future object trajectories as opposed to the current location. Also, tracking methods typically provide only the location of the object of interest in video frames and do not provide any mechanism for an agent to take actions.
We first define our task, visual reaction: the task of interacting with dynamic environments that can evolve independently of the agent. Then, we provide an overview of the model. Finally, we describe each component of the model.
3.1 Task definition
The goal is to learn a policy to catch a thrown object using an agent that moves in 3D space. There is a launcher in the environment that throws objects in the air with different forces in different directions. The agent needs to predict the future trajectory of the object from the past observations (three consecutive RGB images) and take actions at each timestep to intercept the object. An episode is successful if the agent catches the object, i.e. the object lies within the agent’s top-mounted basket, before the object reaches the ground. The trajectories of objects vary depending on their physical properties (e.g., weight, shape, and material). The object might also collide with walls, structures, or other objects, and suddenly change its trajectory.
For each episode, the agent and the launcher start at a random position in the environment (more details in Sec. 4.1). The agent must act quickly to reach the object in a short time before the object hits the floor or goes to rest. This necessitates the use of a forecaster module that should be integrated with the policy of the agent. We consider 20 different object categories such as basketball, newspaper, and bowl (see Sec. A for the complete list).
The model receives ego-centric RGB images from a camera that is mounted on top of the drone agent as input, and outputs an action for each timestep , where, for example, shows acceleration, in meters, along the -axis. The movement of the agent is not deterministic due to the time dependent integration scheme of the physics engine. In the following, we denote the agent and object state by and , respectively. , and denote the position, velocity and acceleration of the drone and , and denote those of the object. and specify the orientation of the agent camera, which can rotate independently from the agent.
3.2 Model Overview
Our model has two main components: a forecaster and a model-predictive planner, as illustrated in Fig. 2. The forecaster receives the visual observations and the estimated agent state at time , and predicts the current state of the thrown object. The forecaster further uses the predicted object state (i.e., position, velocity and acceleration) to forecast steps of object states in the future. The model-predictive planner is responsible for generating the best action for the agent such that it intercepts the thrown object. The model-predictive planner receives the future trajectory of the object from the forecaster and also the current estimate of the agent state as input and outputs the best action accordingly. The model-predictive planner includes an action sampler whose goal is to sample sequences of actions given the current estimate of the agent state, the predicted object trajectory, and the intermediate representation produced by the visual encoder in the forecaster. The action sampler samples actions according to a policy network that is learned. The second component of the model-predictive planner consists of a physics model and a model-predictive controller (MPC). The physics model follows Newton Motion Equation to estimate the next state of the agent (i.e., position and velocity at the next timestep) given the current state and action (that is generated by the action sampler). Our approach builds on related joint model-based and model-free RL ideas. However, instead of an ensemble of model-free and model-based RL for better decision making [24, 59], or using the dynamics model as a data augmentor/imaginer [12, 38] to help the training of model-free RL, we explicitly employ model-free RL to train an action sampler for the model-predictive planner.
In the following, we begin by introducing our forecaster, as shown in Fig. 3(a), along with its training strategy. We then describe how we integrate the forecaster with the model-predictive planner, as presented in Fig. 2 and Fig. 3(b). Finally, we explain how we utilize model-free RL to learn the action distribution used in our planner, Fig. 3(b).
The purpose of the forecaster is to predict the current object state , which includes the position , the velocity , and the acceleration , and then, based on the prediction, forecast future object positions from the most recent three consecutive images . The reason for forecasting timesteps in the future is to enable the planner to employ MPC to select the best action for the task. We show how the horizon length affects the performance in Sec. 4.6. Note that if the agent does not catch the object in the next timestep, we query the forecaster again to predict the trajectory of the object for the next steps. Forecaster also produces the intermediate visual representation , which is used by the action sampler. The details are illustrated in Fig. 3(a). We define the positions, velocity, and acceleration in the agent’s coordinate frame at its starting position.
The three consecutive frames
are passed through a deep convolutional neural network (CNN). The features of the images and the current estimate of the agent stateare combined using an MLP, which results in an embedding . Then, the current state of the object is obtained from through three separate MLPs. The NME, which follows the discretized Newton’s Motion Equation (, ) receives the predicted state of the object to calculate the future positions . We take the derivative of NME and back-propagate the gradients through it in the training phase. Note that NME itself is not learned.
To train the forecaster, we provide the ground truth positions of the thrown object from the environment and obtain the velocity and acceleration by taking the derivative of the positions. We cast the position, velocity, and acceleration prediction as a regression problem and use the L1 loss for optimization.
3.4 Model-predictive Planner
Given the forecasted trajectory of the thrown object, our goal is to control the flying agent to catch the object. We integrate the model-predictive planner with model-free RL to explicitly incorporate the output of the forecaster.
Our proposed model-predictive planner consists of a model-predictive controller (MPC) with a physics model, and an action sampler as illustrated in Fig. 3(b). We will describe how we design the action sampler in Sec. 3.5. The action sampler produces a rollout of future actions. The action is defined as the acceleration of the agent. We sample sequences of actions that are of length from the action distribution. We denote these sequences by . For each action in the sequences, the physics model estimates the next state of the agent given the current state by using the discretized Newton’s Motion Equation (, ). This results in possible trajectories for the agent. Given the forecasted object trajectories , the MPC then selects the best sequence of actions based on the defined objective. The objective for MPC is to select a sequence of actions that minimizes the sum of the distances between the agent and the object over timesteps. We select the first action in the sequence of actions, and the agent executes this action. We feed in the agent’s next state for planning in the next timestep.
Active camera viewpoint. The agent is equipped with a camera that rotates. The angle of the camera is denoted by and
in the agent’s state vector. We use the estimated object and agent position at time , and , to compute the angle of the camera. We calculate the relative position between object and agent by . Then, we obtain the Euler angles along -axis and -axis by and , respectively. In Sec. B, we also show results for the case that the camera is fixed.
3.5 Action sampler
The actions can be sampled from a uniform distribution over the action space or a learned policy network. We take the latter approach and train a policy network which is conditioned on the forecasted object state, current agent state and visual representation. Model-based approaches need to sample a large set of actions at each timestep to achieve a high level of performance. To alleviate this issue, we parameterize our action sampler by a series of MLPs that learns an action distribution given the current agent state, the forecasted trajectory of the objectand the visual representation of observation (refer to Sec. 3.3). This helps to better shape the action distribution, which may result in requiring fewer samples and better performance.
To train our policy network, we utilize policy gradients with the actor-critic algorithm . To provide the reward signal for the policy gradient, we use the ‘success’ signal (if the agent catches the object or not) as a reward. In practice, if the agent succeeds to catch the object before it hits the ground or goes to rest, it would receive a reward of . Furthermore, we also measure the distance between the agent trajectory and the object trajectory as an additional reward signal (pointwise distance at each timestep). As a result, the total reward for each episode is where and are the ground truth positions of the agent and object at time .
We first describe the environment that we use for training and evaluating our model. We then provide results for a set of baselines: different variations of using current state prediction instead of future forecasting and a model-free baseline. We also provide ablation results for our method, where we use uniform sampling instead of the learned action sampler. Moreover, we study how the performance changes with varying mobility of the agent, planning horizon length and number of action sequence samples. Finally, we provide analysis of the results for each object category, different levels of difficulty, and objects unseen during training.
We use AI2-THOR , which is an interactive 3D indoor virtual environment with near photo-realistic scenes. We use the latest version of AI2-THOR (v2.0), which implements physical properties such as object materials, elasticity of various materials, object mass, etc. We develop a new drone-like agent for the environment that can move in three dimensions (the existing agent in AI2-THOR moves only on the ground plane). We also add a launcher that throws objects with random magnitudes in random directions.
The trajectories of the objects vary according to their mass, shape, and material. Sometimes the objects collide with walls or other objects in the scene, which causes sudden changes in the trajectory. Therefore, standard equations of motion are not sufficient to estimate the trajectories, and learning using visual data becomes necessary. The statistics of the average velocity of the trajectory and the number of collisions have been provided in Fig. 4. More information about the physical properties of the objects are in Sec. C.
We augment the drone with a box on top of it for catching objects. The size of drone is with a height of , and the box is with a height of . The drone is equipped with a camera that is able to rotate. The maximum acceleration of the drone is and the maximum velocity is . However, we provide results for different maximum acceleration of the drone. The action for the drone is specified by acceleration in , , and directions. The action space is continuous, but is capped by the maximum acceleration and velocity.
Experiment settings. We use the living room scenes of AI2-THOR for our experiments (30 scenes in total). We follow the common practice for AI2-THOR wherein the first 20 scenes are used for training, the next 5 for validation, and the last 5 for testing. The drone and the launcher are assigned a random position at the beginning of every episode. We set the horizontal relative distance between the launcher and the drone to be meters (any random position). We set the height of the launcher to be meters from the ground which is similar to the average human height. The drone faces the launcher in the beginning of each episode so it observes that an object is being thrown.
To throw the object, the launcher randomly selects a force between newtons, an elevation angle between degree, and an azimuth angle between degree for each episode. The only input to our model at inference time is the ego-centric RGB image from the drone. We use 20 categories of objects such as basketball, alarm clock, and apple for our experiments. We observe different types of trajectories such as parabolic motion, bouncing off the walls and collision with other objects, resulting in sharp changes in the direction. Note that each object category has different physical properties (mass, bounciness, etc.) so the trajectories are quite different. We use the same objects for training and testing. However, the scenes, the positions, the magnitude, and the angle of the throws vary at test time. We also show an experiment, where we test the model on categories unseen during training. We consider 20K trajectories during training, 5K for val and 5K for test. The number of trajectories is uniform across all object categories.
4.2 Implementation details
We train our model by first training the forecaster. Then we freeze the parameters of the forecaster, and train the action sampler. We consider an episode successful if the agent catches the object. We end an episode if the agent succeeds in catching the object, the object falls on the ground, or the length of the episode exceeds steps which is equal to second. We use SGD with initial learning rate of for forecaster learning and decrease it by a factor of every iterations. For the policy network, we employ Adam optimizer  with a learning rate of . We evaluate the framework every iterations on the validation scenes and stop the training when the success rate saturates. We choose MobileNet v2 , which is an efficient and light-weight network as our CNN model. The forecaster outputs the current object position, velocity, and acceleration. The action sampler provides a set of accelerations to the planner. They are all continuous numbers. Sec. D provides details for the architecture of each component of the model.
|N = 100000||N = 10000||N = 1000||N = 100||N = 10||Best|
|Curr. Pos. Predictor (CPP)||17.41||17.19||16.27||13.61||8.04||17.41|
CPP + Kalman Filter
|Model-free (A3C )||-||-||-||-||-||2.51|
|Ours, ME, uniform AS||5.07||5.30||4.07||4.32||3.78||5.30|
|Ours, uniform AS||23.88||22.18||19.77||15.67||8.53||23.88|
|MPC Upper bound||68.67||76.00||78.67||66.00||49.33||78.67|
Current Position Predictor (CPP). This baseline predicts the current position of the object relative to the initial position of the drone in the 3D space, , instead of forecasting the future trajectory. The model-predictive planner receives this predicted position at each time-step and outputs the best action for the drone accordingly. The prediction model is trained by an L1 loss with the same training strategy used for our method.
CPP + Kalman filter. We implement this baseline by introducing the prediction update through time to the Current Position Predictor (CPP) baseline. We assume the change in the position of the object is linear and follows the Markov assumption in a small time period. Thus, we add the Kalman Filter 
right after the output of the CPP. To get the transition probability, we average the displacements along the three dimensions over all the trajectories in the training set. We set the process variance to the standard deviation of the average displacements, and measurement variance to. Further, same as CPP, the model-predictive planner receives this predicted position at each time-step as input and outputs the best action to control the agent. This baseline is expected to be better than CPP, because the Kalman Filter takes into account the possible transitions obtained from the training set so it further smooths out the noisy estimations.
Model-free (A3C ). Another baseline is model-free RL. We use A3C  as our model-free RL baseline. The network architecture for A3C includes the same CNN and MLP used in our forecaster and the action sampler. The network receives images as input and directly outputs action for each time-step. We train A3C by threads and use SharedAdam optimizer with the learning rate of . We run the training for iterations ( millions frames in total). In addition to using the the ‘success’ signal as the reward, we use the distance between the drone and the object as another reward signal.
Motion Equation (ME). The forecaster predicts the position, velocity, and acceleration at the first time-step so we can directly apply motion equation to forecast all future positions. However, since our environment implements complex physical interactions, there are several different types of trajectories (e.g., bouncing or collision). We evaluate if simply using the motion equation is sufficient for capturing such complex behavior.
Uniform Action Sampling (AS). In this ablation study, we replace our action sampler with a sampler that samples actions from a uniform distribution. This ablation shows the effectiveness of learning a sampler in our model.
Quantitative results. The results are summarized in Tab. 1
for all 20 objects and different number of action sequences. We use success rate as our evaluation metric. Recall that the action sampler samplessequences of future actions. We report results for five different values . We set the horizon to for the forecaster and the planner. For evaluation on the test set, we consider episodes for each model. For Tab. 1, we repeat the experiments times and report the average.
As shown in the table, both the current position predictors (CPP) and the Kalman Filter (CPP + Kalman Filter) baseline are outperformed by our model, which shows the effectiveness of forecasting compared to estimating the current position. Our full method outperforms the model-free baseline, which shows the model-based portion of the model helps improving the performance. ‘Ours, ME, uniform AS’ is worse than the two other variations of our method. This shows that simply applying motion equation and ignoring complex physical interactions is insufficient and it confirms that learning from visual data is necessary. We also show that sampling from a learned policy ‘Ours - full’ outperforms ‘Ours, uniform AS’, which samples from a uniform distribution. This justifies using a learned action sampler and shows the effectiveness of the integration of model-free and model-based learning by the model-predictive planner.
Per-category results. Tab. 2 shows the results for each category for ‘Ours - full’ and ‘Ours, uniform AS’. The results show that our model performs better on relatively heavy objects. This is expected since typically there is less variation in the trajectories of heavy objects.
Difficulty-based categorization. Tab. 3 shows the performance achieved by ‘Ours - full’ and ‘Ours, uniform AS’ in terms of difficulty of the trajectory. The difficulty is defined by how many times the object collides with other structures before reaching the ground or being caught by the agent. We define easy by no collision, medium by colliding once, and difficult by more than one collision. The result shows that even though our model outperforms baselines significantly, it is still not as effective for medium and difficult trajectories. It suggests that focusing on modeling more complex physical interactions is important for future research.
|Ours, uniform AS||38.2||4.6||0.7|
Different mobility. We evaluate how varying the mobility of the drone affects the performance (Tab. 4). We define the mobility as the maximum acceleration of the drone. We re-train the model using , , , , of the maximum acceleration.
|Ours, uniform AS||23.88||12.98||11.07||7.09||3.30|
Different Horizon Length. Here, we show how the performance changes with varying the horizon length (Fig. 6). We observe a performance decrease for horizons longer than 3. The reason is that the learned forecaster has a small error and the error for each time-step accumulates. Thus, training an effective model with longer horizons is challenging and we leave it for future research.
Unseen categories. We train the best model on object categories (the list is in the Sec. E) and evaluate on the remaining categories. The success rate is 23.54%. This shows that the model is rather robust to unseen categories.
Qualitative results. Fig. 5 shows two sequences of catching the object and a failure case. The sequence is shown from a third person’s view and the agent camera view (we only use the camera view as the input to our model). The second row shows the drone is still able to catch the object although there is a sudden change in the direction due to the collision of the object with the ceiling. A supplementary video111https://youtu.be/iyAoPuHxvYs shows more success and failure cases.
We address the problem of visual reaction in an interactive and dynamic environment in the context of learning to play catch with a drone. This requies learning to forecast the trajectory of the object and to estimate a sequence of actions to intercept the object before it hits the ground. We propose a new dataset for this task, which is built upon the AI2-THOR framework. We showed that the proposed solution outperforms various baselines and ablations of the model including the variations that do not use forecasting, or do not learn a policy based on the forecasting.
Acknowledgements. We would like to thank Matt Wallingford for valuable feedback and Winson Han and Eli VanderBilt for the design of the drone.
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Appendix A Complete list of objects
We use objects for the experiments: alarm clock, apple, basketball, book, bowl, bread, candle, cup, glass bottle, lettuce, mug, newspaper, salt shaker, soap bottle, statue, tissue box, toaster, toilet paper, vase and watering can.
Appendix B Results for the case that the camera is fixed
In Tab. 5, we provide the results for the case that the drone camera is fixed and does not rotate. In this experiment, we set horizon and number of action sequences . The performance degrades for the case that the camera does not rotate, which is expected.
|Ours, uniform AS||44.54||23.88||9.60|
Appendix C More statistics of object properties
We show more statistics about our dataset in the Fig. 8, including the mass, average acceleration along the trajectories, bounciness, drag, and angular drag. Drag is the tendency of an object to slow down due to friction.
Appendix D Details of the model architecture
Fig. 7 summarizes the details of the model architecture.
Appendix E List of objects for the unseen categories experiment
We selected a subset of objects as our held-out set such that they have different physical properties: basketball, bowl, bread, candle, watering can. We trained our model on the rest of the objects: alarm clock, apple, book, cup, glass bottle, lettuce, mug, newspaper, salt shaker, soap bottle, statue, tissue box, toaster, toilet paper and vase.