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ProbRobScene: A Probabilistic Specification Language for 3D Robotic Manipulation Environments
Robotic control tasks are often first run in simulation for the purposes...
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PILOT: Efficient Planning by Imitation Learning and Optimisation for Safe Autonomous Driving
Achieving the right balance between planning quality, safety and runtime...
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Affordance-Aware Handovers with Human Arm Mobility Constraints
Reasoning about object handover configurations allows an assistive agent...
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Counterfactual Explanation and Causal Inference in Service of Robustness in Robot Control
We propose an architecture for training generative models of counterfact...
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Residual Learning from Demonstration
Contacts and friction are inherent to nearly all robotic manipulation ta...
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Action sequencing using visual permutations
Humans can easily reason about the sequence of high level actions needed...
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Semi-supervised Learning From Demonstration Through Program Synthesis: An Inspection Robot Case Study
Semi-supervised learning improves the performance of supervised machine ...
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Self-Assessment of Grasp Affordance Transfer
Reasoning about object grasp affordances allows an autonomous agent to e...
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Learning from Demonstration with Weakly Supervised Disentanglement
Robotic manipulation tasks, such as wiping with a soft sponge, require c...
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From Demonstrations to Task-Space Specifications: Using Causal Analysis to Extract Rule Parameterization from Demonstrations
Learning models of user behaviour is an important problem that is broadl...
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Affordances in Robotic Tasks – A Survey
Affordances are key attributes of what must be perceived by an autonomou...
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Integrating Planning and Interpretable Goal Recognition for Autonomous Driving
The ability to predict the intentions and driving trajectories of other ...
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A Two-Stage Optimization Approach to Safe-by-Design Planning for Autonomous Driving
Lessons learned from the increasing diversity of road trial deployments ...
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Learning robotic ultrasound scanning using probabilistic temporal ranking
This paper addresses a common class of problems where a robot learns to ...
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Elaborating on Learned Demonstrations with Temporal Logic Specifications
Most current methods for learning from demonstrations assume that those ...
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Lower Dimensional Kernels for Video Discriminators
This work presents an analysis of the discriminators used in Generative ...
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Learning with Modular Representations for Long-Term Multi-Agent Motion Predictions
Context plays a significant role in the generation of motion for dynamic...
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Surfing on an uncertain edge: Precision cutting of soft tissue using torque-based medium classification
Precision cutting of soft-tissue remains a challenging problem in roboti...
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Disentangled Relational Representations for Explaining and Learning from Demonstration
Learning from demonstration is an effective method for human users to in...
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E-HBA: Using Action Policies for Expert Advice and Agent Typification
Past research has studied two approaches to utilise predefined policy se...
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Comparative Evaluation of Multiagent Learning Algorithms in a Diverse Set of Ad Hoc Team Problems
This paper is concerned with evaluating different multiagent learning (M...
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Composing Diverse Policies for Temporally Extended Tasks
Temporally extended and sequenced robot motion tasks are often character...
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Vid2Param: Online system identification from video for robotics applications
Robots performing tasks in dynamic environments would benefit greatly fr...
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On Convergence and Optimality of Best-Response Learning with Policy Types in Multiagent Systems
While many multiagent algorithms are designed for homogeneous systems (i...
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An Empirical Study on the Practical Impact of Prior Beliefs over Policy Types
Many multiagent applications require an agent to learn quickly how to in...
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Exploiting Causality for Selective Belief Filtering in Dynamic Bayesian Networks (Extended Abstract)
Dynamic Bayesian networks (DBNs) are a general model for stochastic proc...
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Hybrid system identification using switching density networks
Behaviour cloning is a commonly used strategy for imitation learning and...
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DynoPlan: Combining Motion Planning and Deep Neural Network based Controllers for Safe HRL
Many realistic robotics tasks are best solved compositionally, through c...
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Learning Grasp Affordance Reasoning through Semantic Relations
Reasoning about object affordances allows an autonomous agent to perform...
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Iterative Model-Based Reinforcement Learning Using Simulations in the Differentiable Neural Computer
We propose a lifelong learning architecture, the Neural Computer Agent (...
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Reasoning on Grasp-Action Affordances
Artificial intelligence is essential to succeed in challenging activitie...
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Towards Evaluating and Understanding Robust Optimisation under Transfer
This work evaluates the efficacy of adversarial robustness under transfe...
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Learning Programmatically Structured Representations with Perceptor Gradients
We present the perceptor gradients algorithm -- a novel approach to lear...
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To Stir or Not to Stir: Online Estimation of Liquid Properties for Pouring Actions
Our brains are able to exploit coarse physical models of fluids to solve...
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Using Causal Analysis to Learn Specifications from Task Demonstrations
Learning models of user behaviour is an important problem that is broadl...
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From explanation to synthesis: Compositional program induction for learning from demonstration
Hybrid systems are a compact and natural mechanism with which to address...
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Learning Best Response Strategies for Agents in Ad Exchanges
Ad exchanges are widely used in platforms for online display advertising...
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Active Localization of Gas Leaks using Fluid Simulation
Sensors are routinely mounted on robots to acquire various forms of meas...
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Interpretable Latent Spaces for Learning from Demonstration
Effective human-robot interaction, such as in robot learning from human ...
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Efficient Computation of Collision Probabilities for Safe Motion Planning
We address the problem of safe motion planning. As mobile robots and aut...
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The ORCA Hub: Explainable Offshore Robotics through Intelligent Interfaces
We present the UK Robotics and Artificial Intelligence Hub for Offshore ...
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Reasoning about Unforeseen Possibilities During Policy Learning
Methods for learning optimal policies in autonomous agents often assume ...
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Using Program Induction to Interpret Transition System Dynamics
Explaining and reasoning about processes which underlie observed black-b...
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Grounding Symbols in Multi-Modal Instructions
As robots begin to cohabit with humans in semi-structured environments, ...
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Explaining Transition Systems through Program Induction
Explaining and reasoning about processes which underlie observed black-b...
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Estimating Activity at Multiple Scales using Spatial Abstractions
Autonomous robots operating in dynamic environments must maintain belief...
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Belief and Truth in Hypothesised Behaviours
There is a long history in game theory on the topic of Bayesian or "rati...
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A Game-Theoretic Model and Best-Response Learning Method for Ad Hoc Coordination in Multiagent Systems
The ad hoc coordination problem is to design an autonomous agent which i...
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Bayesian Policy Reuse
A long-lived autonomous agent should be able to respond online to novel ...
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Exploiting Causality for Selective Belief Filtering in Dynamic Bayesian Networks
Dynamic Bayesian networks (DBNs) are a general model for stochastic proc...
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