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Rishabh Jangir

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3 papers

2 author rows

ICLR Conference 2021 Conference Paper

Self-Supervised Policy Adaptation during Deployment

Nicklas Hansen 0001
Rishabh Jangir
Yu Sun 0020
Guillem Alenyà
Pieter Abbeel
Alexei A. Efros
Lerrel Pinto
Xiaolong Wang 0004

In most real world scenarios, a policy trained by reinforcement learning in one environment needs to be deployed in another, potentially quite different environment. However, generalization across different environments is known to be hard. A natural solution would be to keep training after deployment in the new environment, but this cannot be done if the new environment offers no reward signal. Our work explores the use of self-supervision to allow the policy to continue training after deployment without using any rewards. While previous methods explicitly anticipate changes in the new environment, we assume no prior knowledge of those changes yet still obtain significant improvements. Empirical evaluations are performed on diverse simulation environments from DeepMind Control suite and ViZDoom, as well as real robotic manipulation tasks in continuously changing environments, taking observations from an uncalibrated camera. Our method improves generalization in 31 out of 36 environments across various tasks and outperforms domain randomization on a majority of environments. Webpage and implementation: https://nicklashansen.github.io/PAD/.

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ICRA Conference 2020 Conference Paper

Dynamic Cloth Manipulation with Deep Reinforcement Learning

Rishabh Jangir
Guillem Alenyà
Carme Torras

In this paper we present a Deep Reinforcement Learning approach to solve dynamic cloth manipulation tasks. Differing from the case of rigid objects, we stress that the followed trajectory (including speed and acceleration) has a decisive influence on the final state of cloth, which can greatly vary even if the positions reached by the grasped points are the same. We explore how goal positions for non-grasped points can be attained through learning adequate trajectories for the grasped points. Our approach uses few demonstrations to improve control policy learning, and a sparse reward approach to avoid engineering complex reward functions. Since perception of textiles is challenging, we also study different state representations to assess the minimum observation space required for learning to succeed. Finally, we compare different combinations of control policy encodings, demonstrations, and sparse reward learning techniques, and show that our proposed approach can learn dynamic cloth manipulation in an efficient way, i. e. , using a reduced observation space, a few demonstrations, and a sparse reward.

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AAMAS Conference 2017 Conference Paper

Data Driven Strategies for Active Monocular SLAM using Inverse Reinforcement Learning

Vignesh Prasad
Rishabh Jangir
Ravindran Balaraman
K. Madhava Krishna

Learning a complex task like robot maneuver while preventing Monocular SLAM failure is challenging for both robots and humans. We devise a computational model for representing and inferring strategies for this task, formulated as a Markov Decision Process (MDP). We show how the reward function can be learned using Inverse Reinforcement Learning. The resulting framework allows us to understand how chosen parameters affect the quality of Monocular SLAM. A significant improvement in performance as compared to other state-of-the-art methods is also shown.

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