4 citations · 5 across the 5 of their papers we have counts for
5 papers
FLEX: A Framework for Learning Robot-Agnostic Force-based Skills Involving Sustained Contact Object Manipulation
Shijie Fang, Wenchang Gao, Shivam Goel +3
Learning to manipulate objects efficiently, particularly those involving sustained contact (e.g., pushing, sliding) and articulated parts (e.g., drawers, doors), presents significa…
NovelGym: A Flexible Ecosystem for Hybrid Planning and Learning Agents Designed for Open Worlds
Shivam Goel, Yichen Wei, Panagiotis Lymperopoulos +3
As AI agents leave the lab and venture into the real world as autonomous vehicles, delivery robots, and cooking robots, it is increasingly necessary to design and comprehensively e…
A Framework for Few-Shot Policy Transfer through Observation Mapping and Behavior Cloning
Yash Shukla, Bharat Kesari, Shivam Goel +2
Despite recent progress in Reinforcement Learning for robotics applications, many tasks remain prohibitively difficult to solve because of the expensive interaction cost. Transfer…
RAPid-Learn: A Framework for Learning to Recover for Handling Novelties in Open-World Environments
Shivam Goel, Yash Shukla, Vasanth Sarathy +2
We propose RAPid-Learn: Learning to Recover and Plan Again, a hybrid planning and learning method, to tackle the problem of adapting to sudden and unexpected changes in an agent's…
SPOTTER: Extending Symbolic Planning Operators through Targeted Reinforcement Learning
Vasanth Sarathy, Daniel Kasenberg, Shivam Goel +2
Symbolic planning models allow decision-making agents to sequence actions in arbitrary ways to achieve a variety of goals in dynamic domains. However, they are typically handcrafte…