864 papers
This paper proposes a biologically inspired framework for online Continual Reinforcement Learning that leverages world model prediction residuals to automatically detect environmental changes and trigger self-adapting finetuning in robotic agents, enabling them to improve their performance during deployment without external supervision.
The paper proposes Sim2Sea, a comprehensive framework featuring a GPU-accelerated simulator, a dual-stream spatiotemporal policy with safety-guided action masking, and targeted domain randomization, which successfully enables zero-shot sim-to-real transfer of autonomous navigation for a 17-ton unmanned vessel in congested maritime waters.
SaFeR is a novel framework for generating safety-critical autonomous driving test scenarios that balances adversarial criticality, physical feasibility, and behavioral realism by employing a Transformer-based realism prior with a differential attention mechanism and a feasibility-constrained token resampling strategy derived from offline reinforcement learning.
GarmentPile++ is a novel pipeline that integrates vision-language reasoning with visual affordance perception and dual-arm cooperation to enable safe, precise retrieval of single garments from cluttered piles, bridging the gap between single-garment manipulation research and real-world scenarios.
This paper presents a physics-based neuromusculoskeletal learning framework that trains a hip-exoskeleton control policy entirely in simulation using reinforcement learning and muscle-synergy priors, successfully transferring the policy to hardware without motion-capture data or additional tuning while achieving significant reductions in muscle activation and joint power across diverse walking conditions.
This paper introduces PRAM-R, a unified framework that leverages an LLM-guided router and hierarchical memory within an asynchronous dual-loop architecture to dynamically optimize sensor modality usage, significantly reducing computational costs and routing instability while maintaining high trajectory accuracy in autonomous driving.
This paper introduces VANGUARD, a lightweight geometric perception tool that enables LLM-based UAV agents operating in GPS-denied environments to accurately estimate Ground Sample Distance and recover metric scale by leveraging detected vehicles as environmental anchors, thereby significantly reducing spatial hallucinations and catastrophic failures compared to state-of-the-art vision-language models.
This paper introduces RoboCasa365, a large-scale simulation framework featuring 365 everyday tasks across 2,500 diverse kitchen environments and extensive human and synthetic demonstration data, designed to provide a reproducible benchmark for evaluating and advancing generalist robot policies through systematic analysis of task diversity, dataset scale, and environment variation.