arXiv subjects

Shreyas S. Raman

Publications and source records attributed to Shreyas S. Raman.

2 recordsLinked to original sources

arXiv2026

SkillWrapper: Generative Predicate Invention for Task-level Robot Planning

Generalizing from individual skill executions to long-horizon tasks is a core challenge in building autonomous robots. A promising direction is learning high-level, symbolic representations of low-level robot skills, enabling abstract reasoning independent of the low-level state space. Recent advances in foundation models have made it possible to generate symbolic predicates that operate on raw sensory inputs-a process we call generative predicate invention-to facilitate downstream representation learning. However, prior work learns these abstractions using heuristic or ad-hoc procedures, leaving unclear which formal properties they ought to satisfy, and how these properties can guide representation learning. We address these questions by characterizing conditions under which learned representations support sound and complete task-level planning, and using them to guide the design of SkillWrapper, a system that autonomously learns symbolic representations of black-box skills without predefined tasks, predicates, or operators. Our approach leverages foundation models to actively collect robot data and learn human-interpretable, plannable representations directly from RGB observations. Our extensive empirical evaluation in simulation and on real robots shows that SkillWrapper learns abstract representations that enable robots to compose black-box skills to solve unseen, long-horizon tasks in the real world.

cs.RO↗

arXiv2023

Plug in the Safety Chip: Enforcing Constraints for LLM-driven Robot Agents

Recent advancements in large language models (LLMs) have enabled a new research domain, LLM agents, for solving robotics and planning tasks by leveraging the world knowledge and general reasoning abilities of LLMs obtained during pretraining. However, while considerable effort has been made to teach the robot the "dos," the "don'ts" received relatively less attention. We argue that, for any practical usage, it is as crucial to teach the robot the "don'ts": conveying explicit instructions about prohibited actions, assessing the robot's comprehension of these restrictions, and, most importantly, ensuring compliance. Moreover, verifiable safe operation is essential for deployments that satisfy worldwide standards such as ISO 61508, which defines standards for safely deploying robots in industrial factory environments worldwide. Aiming at deploying the LLM agents in a collaborative environment, we propose a queryable safety constraint module based on linear temporal logic (LTL) that simultaneously enables natural language (NL) to temporal constraints encoding, safety violation reasoning and explaining, and unsafe action pruning. To demonstrate the effectiveness of our system, we conducted experiments in VirtualHome environment and on a real robot. The experimental results show that our system strictly adheres to the safety constraints and scales well with complex safety constraints, highlighting its potential for practical utility.

cs.RO↗