4 papers
Improving Fine-Grained Manipulation through Demonstration Expansion and Execution Design
Jingxu Gu, Dongze Lian
Fine-grained manipulation requires a robot policy to follow instructions, remain robust to visual variations, and translate predicted actions into precise robot control. We present…
TACTIC: Task-Agnostic Contrastive pre-Training for Inter-Agent Communication
Peihong Yu, Manav Mishra, Syed Zaidi +1
The "sight range dilemma" in cooperative Multi-Agent Reinforcement Learning (MARL) presents a significant challenge: limited observability hinders team coordination, while extensiv…
Deep Reinforcement Learning-Based Approach for a Single Vehicle Persistent Surveillance Problem with Fuel Constraints
Manav Mishra, Hritik Bana, Saswata Sarkar +3
This article presents a deep reinforcement learning-based approach to tackle a persistent surveillance mission requiring a single unmanned aerial vehicle initially stationed at a d…
Beyond Joint Demonstrations: Personalized Expert Guidance for Efficient Multi-Agent Reinforcement Learning
Peihong Yu, Manav Mishra, Alec Koppel +5
Multi-Agent Reinforcement Learning (MARL) algorithms face the challenge of efficient exploration due to the exponential increase in the size of the joint state-action space. While…