b'Animesh Garg'

research

∙ 06/29/2022

Neural Motion Fields: Encoding Grasp Trajectories as Implicit Value Functions

The pipeline of current robotic pick-and-place methods typically consist...

Yun-Chun Chen, et al. ∙

research

∙ 06/17/2022

SMPL: Simulated Industrial Manufacturing and Process Control Learning Environments

Traditional biological and pharmaceutical manufacturing plants are contr...

Mohan Zhang, et al. ∙

research

∙ 05/30/2022

Neural Shape Mating: Self-Supervised Object Assembly with Adversarial Shape Priors

Learning to autonomously assemble shapes is a crucial skill for many rob...

Yun-Chun Chen, et al. ∙

research

∙ 04/14/2022

Accelerated Policy Learning with Parallel Differentiable Simulation

Deep reinforcement learning can generate complex control policies, but r...

Jie Xu, et al. ∙

research

∙ 04/04/2022

Value Gradient weighted Model-Based Reinforcement Learning

Model-based reinforcement learning (MBRL) is a sample efficient techniqu...

Claas Voelcker, et al. ∙

research

∙ 03/28/2022

X-Pool: Cross-Modal Language-Video Attention for Text-Video Retrieval

In text-video retrieval, the objective is to learn a cross-modal similar...

Satya Krishna Gorti, et al. ∙

research

∙ 03/19/2022

DiSECt: A Differentiable Simulator for Parameter Inference and Control in Robotic Cutting

Robotic cutting of soft materials is critical for applications such as f...

Eric Heiden, et al. ∙

research

∙ 02/23/2022

Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning

Offline Reinforcement Learning (RL) aims to learn policies from previous...

Chenjia Bai, et al. ∙

research

∙ 11/02/2021

Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics

Offline reinforcement learning leverages large datasets to train policie...

Matthias Weissenbacher, et al. ∙

research

∙ 10/30/2021

Convergence and Optimality of Policy Gradient Methods in Weakly Smooth Settings

Policy gradient methods have been frequently applied to problems in cont...

Matthew Shunshi Zhang, et al. ∙

research

∙ 10/27/2021

Reinforcement Learning in Factored Action Spaces using Tensor Decompositions

We present an extended abstract for the previously published work TESSER...

Anuj Mahajan, et al. ∙

research

∙ 10/20/2021

Dynamic Bottleneck for Robust Self-Supervised Exploration

Exploration methods based on pseudo-count of transitions or curiosity of...

Chenjia Bai, et al. ∙

research

∙ 10/05/2021

Continuous-Time Fitted Value Iteration for Robust Policies

Solving the Hamilton-Jacobi-Bellman equation is important in many domain...

Michael Lutter, et al. ∙

research

∙ 09/30/2021

Seeing Glass: Joint Point Cloud and Depth Completion for Transparent Objects

The basis of many object manipulation algorithms is RGB-D input. Yet, co...

Haoping Xu, et al. ∙

research

∙ 09/25/2021

Auditing AI models for Verified Deployment under Semantic Specifications

Auditing trained deep learning (DL) models prior to deployment is vital ...

Homanga Bharadhwaj, et al. ∙

research

∙ 09/10/2021

PlaTe: Visually-Grounded Planning with Transformers in Procedural Tasks

In this work, we study the problem of how to leverage instructional vide...

Jiankai Sun, et al. ∙

research

∙ 08/26/2021

Drop-DTW: Aligning Common Signal Between Sequences While Dropping Outliers

In this work, we consider the problem of sequence-to-sequence alignment ...

Nikita Dvornik, et al. ∙

research

∙ 08/22/2021

Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFinger

We present a system for learning a challenging dexterous manipulation ta...

Arthur Allshire, et al. ∙

research

∙ 07/14/2021

Centralized Model and Exploration Policy for Multi-Agent RL

Reinforcement learning (RL) in partially observable, fully cooperative m...

Qizhen Zhang, et al. ∙

research

∙ 07/12/2021

A Persistent Spatial Semantic Representation for High-level Natural Language Instruction Execution

Natural language provides an accessible and expressive interface to spec...

Valts Blukis, et al. ∙

research

∙ 07/06/2021

Learning Latent Actions to Control Assistive Robots

Assistive robot arms enable people with disabilities to conduct everyday...

Dylan P. Losey, et al. ∙

research

∙ 06/28/2021

GIFT: Generalizable Interaction-aware Functional Tool Affordances without Labels

Tool use requires reasoning about the fit between an object's affordance...

Dylan Turpin, et al. ∙

research

∙ 06/08/2021

Neural Hybrid Automata: Learning Dynamics with Multiple Modes and Stochastic Transitions

Effective control and prediction of dynamical systems often require appr...

Michael Poli, et al. ∙

research

∙ 05/31/2021

Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning

Reinforcement Learning in large action spaces is a challenging problem. ...

Anuj Mahajan, et al. ∙

research

∙ 05/25/2021

DiSECt: A Differentiable Simulation Engine for Autonomous Robotic Cutting

Robotic cutting of soft materials is critical for applications such as f...

Eric Heiden, et al. ∙

research

∙ 05/25/2021

Robust Value Iteration for Continuous Control Tasks

When transferring a control policy from simulation to a physical system,...

Michael Lutter, et al. ∙

research

∙ 05/18/2021

Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team Composition

In real-world multiagent systems, agents with different capabilities may...

Bo Liu, et al. ∙

research

∙ 05/13/2021

Principled Exploration via Optimistic Bootstrapping and Backward Induction

One principled approach for provably efficient exploration is incorporat...

Chenjia Bai, et al. ∙

research

∙ 05/10/2021

Value Iteration in Continuous Actions, States and Time

Classical value iteration approaches are not applicable to environments ...

Michael Lutter, et al. ∙

research

∙ 04/20/2021

GLiDE: Generalizable Quadrupedal Locomotion in Diverse Environments with a Centroidal Model

Model-free reinforcement learning (RL) for legged locomotion commonly re...

Zhaoming Xie, et al. ∙

research

∙ 03/29/2021

LASER: Learning a Latent Action Space for Efficient Reinforcement Learning

The process of learning a manipulation task depends strongly on the acti...

Arthur Allshire, et al. ∙

research

∙ 03/18/2021

Articulated Object Interaction in Unknown Scenes with Whole-Body Mobile Manipulation

A kitchen assistant needs to operate human-scale objects, such as cabine...

Mayank Mittal, et al. ∙

research

∙ 03/10/2021

S4RL: Surprisingly Simple Self-Supervision for Offline Reinforcement Learning

Offline reinforcement learning proposes to learn policies from large col...

Samarth Sinha, et al. ∙

research

∙ 01/18/2021

Learning by Watching: Physical Imitation of Manipulation Skills from Human Videos

We present an approach for physical imitation from human videos for robo...

Haoyu Xiong, et al. ∙

research

∙ 12/22/2020

Emergent Hand Morphology and Control from Optimizing Robust Grasps of Diverse Objects

Evolution in nature illustrates that the creatures' biological structure...

Xinlei Pan, et al. ∙

research

∙ 11/27/2020

Skill Transfer via Partially Amortized Hierarchical Planning

To quickly solve new tasks in complex environments, intelligent agents n...

Kevin Xie, et al. ∙

research

∙ 11/24/2020

C-Learning: Horizon-Aware Cumulative Accessibility Estimation

Multi-goal reaching is an important problem in reinforcement learning ne...

Panteha Naderian, et al. ∙

research

∙ 11/23/2020

Action Concept Grounding Network for Semantically-Consistent Video Generation

Recent works in self-supervised video prediction have mainly focused on ...

Wei Yu, et al. ∙

research

∙ 11/14/2020

Solving Physics Puzzles by Reasoning about Paths

We propose a new deep learning model for goal-driven tasks that require ...

Augustin Harter, et al. ∙

research

∙ 11/04/2020

Dynamics Randomization Revisited:A Case Study for Quadrupedal Locomotion

Understanding the gap between simulation andreality is critical for rein...

Zhaoming Xie, et al. ∙

research

∙ 10/27/2020

Conservative Safety Critics for Exploration

Safe exploration presents a major challenge in reinforcement learning (R...

Homanga Bharadhwaj, et al. ∙

research

∙ 10/19/2020

D2RL: Deep Dense Architectures in Reinforcement Learning

While improvements in deep learning architectures have played a crucial ...

Samarth Sinha, et al. ∙

research

∙ 09/21/2020

Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion

We present a hierarchical framework that combines model-based control an...

Xingye Da, et al. ∙

research

∙ 08/17/2020

OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation

Real-world tasks often exhibit a compositional structure that contains a...

Hongyu Ren, et al. ∙

research

∙ 08/13/2020

Visuomotor Mechanical Search: Learning to Retrieve Target Objects in Clutter

When searching for objects in cluttered environments, it is often necess...

Andrey Kurenkov, et al. ∙

research

∙ 07/06/2020

Counterfactual Data Augmentation using Locally Factored Dynamics

Many dynamic processes, including common scenarios in robotic control an...

Silviu Pitis, et al. ∙

research

∙ 07/01/2020

Causal Discovery in Physical Systems from Videos

Causal discovery is at the core of human cognition. It enables us to rea...

Yunzhu Li, et al. ∙

research

∙ 07/01/2020

De-anonymization of authors through arXiv submissions during double-blind review

In this paper, we investigate the effects of releasing arXiv preprints o...

Homanga Bharadhwaj, et al. ∙

research

∙ 06/30/2020

Maximum Entropy Models for Fast Adaptation

Deep Neural Networks have shown great promise on a variety of downstream...

Samarth Sinha, et al. ∙

research

∙ 06/23/2020

Experience Replay with Likelihood-free Importance Weights

The use of past experiences to accelerate temporal difference (TD) learn...

Samarth Sinha, et al. ∙

Animesh Garg

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