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ExACT: An End-to-End Autonomous Excavator System Using Action Chunking With Transformers
Excavators are crucial for diverse tasks such as construction and mining, while autonomous excavator systems enhance safety and efficiency, address labor shortages, and improve human working conditions.
RLingua: Improving Reinforcement Learning Sample Efficiency in Robotic Manipulations With Large Language Models
In this paper, we propose RLingua, a framework that can leverage the internal knowledge of large language models (LLMs) to reduce the sample complexity of RL in robotic manipulations.
GOATS: Goal Sampling Adaptation for Scooping with Curriculum Reinforcement Learning
In this work, we first formulate the problem of robotic water scooping using goal-conditioned reinforcement learning.
Contact Pose Identification for Peg-in-Hole Assembly under Uncertainties
Peg-in-hole assembly is a challenging contact-rich manipulation task.
Robotics
