Learning Tool-Aware Adaptive Compliant Control for Autonomous Regolith Excavation

• URL: http://arxiv.org/abs/2509.05475v1
• Date: Fri, 05 Sep 2025 20:09:28 GMT
• Title: Learning Tool-Aware Adaptive Compliant Control for Autonomous Regolith Excavation
• Authors: Andrej Orsula, Matthieu Geist, Miguel Olivares-Mendez, Carol Martinez,
• Abstract summary: This work introduces a framework where a model-based reinforcement learning agent learns within a parallelized simulation.<n>We show that training with a procedural distribution of tools is critical for generalization and enables the development of sophisticated tool-aware behavior.
• Score: 16.948852537273655
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous regolith excavation is a cornerstone of in-situ resource utilization for a sustained human presence beyond Earth. However, this task is fundamentally hindered by the complex interaction dynamics of granular media and the operational need for robots to use diverse tools. To address these challenges, this work introduces a framework where a model-based reinforcement learning agent learns within a parallelized simulation. This environment leverages high-fidelity particle physics and procedural generation to create a vast distribution of both lunar terrains and excavation tool geometries. To master this diversity, the agent learns an adaptive interaction strategy by dynamically modulating its own stiffness and damping at each control step through operational space control. Our experiments demonstrate that training with a procedural distribution of tools is critical for generalization and enables the development of sophisticated tool-aware behavior. Furthermore, we show that augmenting the agent with visual feedback significantly improves task success. These results represent a validated methodology for developing the robust and versatile autonomous systems required for the foundational tasks of future space missions.

Related papers

• Tool-R0: Self-Evolving LLM Agents for Tool-Learning from Zero Data [49.315842374696295]
  Large language models (LLMs) are becoming the foundation for autonomous agents that can use tools to solve complex tasks.<n>We propose Tool-R0 framework for training general purpose tool-calling agents from scratch with self-play RL.<n>Our work further provides empirical insights into self-play LLM agents by analyzing co-evolution, curriculum dynamics, and scaling behavior.
  arXiv  Detail & Related papers  (2026-02-24T19:41:18Z)
• ASTRA: Automated Synthesis of agentic Trajectories and Reinforcement Arenas [13.919124676472022]
  ASTRA is an end-to-end framework for training tool-augmented language model agents.<n>ASTRA integrates scalable data synthesis and verifiable reinforcement learning.<n> Experiments on multiple agentic tool-use benchmarks demonstrate that ASTRA-trained models achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2026-01-29T11:22:23Z)
• Space Robotics Bench: Robot Learning Beyond Earth [16.948852537273655]
  Space Robotics Bench is an open-source simulation framework for robot learning in space.<n>It integrates on-demand procedural generation with massively parallel simulation environments.<n>It includes a comprehensive suite of benchmark tasks that span a wide range of mission-relevant scenarios.
  arXiv  Detail & Related papers  (2025-09-27T14:28:31Z)
• Sim2Dust: Mastering Dynamic Waypoint Tracking on Granular Media [19.76267553432585]
  This work presents a complete sim-to-real framework for developing robust control policies for dynamic waypoint tracking on challenging surfaces.<n>We leverage massively parallel simulation to train reinforcement learning agents across a vast distribution of procedurally generated environments.<n>Our experiments systematically compare multiple reinforcement learning algorithms and action smoothing filters to identify the most effective combinations for real-world deployment.
  arXiv  Detail & Related papers  (2025-08-15T14:30:07Z)
• Feedback-Driven Tool-Use Improvements in Large Language Models via Automated Build Environments [70.42705564227548]
  We propose an automated environment construction pipeline for large language models (LLMs)<n>This enables the creation of high-quality training environments that provide detailed and measurable feedback without relying on external tools.<n>We also introduce a verifiable reward mechanism that evaluates both the precision of tool use and the completeness of task execution.
  arXiv  Detail & Related papers  (2025-08-12T09:45:19Z)
• RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation [68.70755196744533]
  RoboGen is a generative robotic agent that automatically learns diverse robotic skills at scale via generative simulation. Our work attempts to extract the extensive and versatile knowledge embedded in large-scale models and transfer them to the field of robotics.
  arXiv  Detail & Related papers  (2023-11-02T17:59:21Z)
• Orchestration of Emulator Assisted Mobile Edge Tuning for AI Foundation Models: A Multi-Agent Deep Reinforcement Learning Approach [10.47302625959368]
  We present a groundbreaking paradigm integrating Mobile Edge Computing with foundation models, specifically designed to enhance local task performance on user equipment (UE) Central to our approach is the innovative Emulator-Adapter architecture, segmenting the foundation model into two cohesive modules. We introduce an advanced resource allocation mechanism that is fine-tuned to the needs of the Emulator-Adapter structure in decentralized settings.
  arXiv  Detail & Related papers  (2023-10-26T15:47:51Z)
• Bridging Active Exploration and Uncertainty-Aware Deployment Using Probabilistic Ensemble Neural Network Dynamics [11.946807588018595]
  This paper presents a unified model-based reinforcement learning framework that bridges active exploration and uncertainty-aware deployment. The two opposing tasks of exploration and deployment are optimized through state-of-the-art sampling-based MPC. We conduct experiments on both autonomous vehicles and wheeled robots, showing promising results for both exploration and deployment.
  arXiv  Detail & Related papers  (2023-05-20T17:20:12Z)
• CLAS: Coordinating Multi-Robot Manipulation with Central Latent Action Spaces [9.578169216444813]
  This paper proposes an approach to coordinating multi-robot manipulation through learned latent action spaces that are shared across different agents. We validate our method in simulated multi-robot manipulation tasks and demonstrate improvement over previous baselines in terms of sample efficiency and learning performance.
  arXiv  Detail & Related papers  (2022-11-28T23:20:47Z)
• Multitask Adaptation by Retrospective Exploration with Learned World Models [77.34726150561087]
  We propose a meta-learned addressing model called RAMa that provides training samples for the MBRL agent taken from task-agnostic storage. The model is trained to maximize the expected agent's performance by selecting promising trajectories solving prior tasks from the storage.
  arXiv  Detail & Related papers  (2021-10-25T20:02:57Z)
• GEM: Group Enhanced Model for Learning Dynamical Control Systems [78.56159072162103]
  We build effective dynamical models that are amenable to sample-based learning. We show that learning the dynamics on a Lie algebra vector space is more effective than learning a direct state transition model. This work sheds light on a connection between learning of dynamics and Lie group properties, which opens doors for new research directions.
  arXiv  Detail & Related papers  (2021-04-07T01:08:18Z)
• ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation [99.2543521972137]
  ReLMoGen is a framework that combines a learned policy to predict subgoals and a motion generator to plan and execute the motion needed to reach these subgoals. Our method is benchmarked on a diverse set of seven robotics tasks in photo-realistic simulation environments. ReLMoGen shows outstanding transferability between different motion generators at test time, indicating a great potential to transfer to real robots.
  arXiv  Detail & Related papers  (2020-08-18T08:05:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.