665 papers
This paper introduces CuriousBot, a mobile exploration system that utilizes a 3D relational object graph to enable active interaction with diverse objects in complex environments, outperforming vision-language model-based approaches in both effectiveness and generalization.
This paper introduces MS-HGNN, a morphological-symmetry-equivariant heterogeneous graph neural network that integrates robotic kinematic structures and symmetries as architectural constraints to achieve high generalizability and efficiency in learning dynamics for various multi-body systems, with its effectiveness validated through formal proofs and experiments on quadruped robots.
This paper proposes a data-driven framework combining control barrier functions and differentiable optimization to learn interpretable responsibility allocations, enabling autonomous agents to quantify and adjust their behaviors for safe, socially-aware multi-agent interactions.
The paper introduces Sensor-Conditioned Diffusion Policies (SCDP), a novel framework that enables robust humanoid locomotion using only onboard sensors by distilling privileged full-body knowledge through mixed-observation training and specialized denoising techniques, successfully achieving near-perfect simulation performance and real-world deployment on a G1 robot without explicit state estimation.
This paper introduces Quality over Quantity (QoQ), a systematic framework that leverages influence functions to automatically curate high-quality robot learning demonstrations by quantifying each sample's contribution to reducing validation loss, thereby significantly improving policy performance over manual or heuristic data selection methods.
This paper demonstrates that channel-centric models, including ray-tracing simulators, fail to accurately predict end-to-end throughput in private 5G networks due to systematic over-estimation of MIMO spatial layers, whereas data-driven Gaussian process models trained on direct measurements provide significantly more reliable predictions for communication-aware robot planning.
This paper proposes \textsc{applv}, a novel framework that leverages Vision-Language-Action models to dynamically predict and adapt classical planner parameters, thereby achieving superior navigation performance and generalization in highly constrained environments compared to existing methods.
The paper introduces SPREAD, a geometry-preserving framework for lifelong imitation learning that utilizes singular value decomposition to align policy representations within low-rank subspaces and a confidence-guided distillation strategy to mitigate catastrophic forgetting while achieving state-of-the-art performance on the LIBERO benchmark.
The paper proposes SPARC, a spatial-aware path planning framework that introduces a Relation-enhanced Multi-Head Attention (RMHA) mechanism to explicitly encode pairwise distances into robot communication, significantly improving decentralized multi-robot coordination and zero-shot generalization in high-density environments compared to existing methods.
Pri4R is a simple yet effective method that enhances Vision-Language-Action models with an implicit understanding of world dynamics by training them to predict 3D point tracks using privileged 4D information, thereby significantly improving physical manipulation performance without adding inference overhead.
This paper introduces SynHLMA, a novel framework that synthesizes hand manipulation sequences for articulated objects by aligning natural language instructions with a discrete human-object interaction representation, thereby enabling robust grasp generation, prediction, and interpolation for applications in embodied AI and robotics.
FALCON addresses the spatial reasoning limitations of existing 2D-based vision-language-action models by leveraging spatial foundation models to inject rich 3D geometric priors directly into the action head, achieving state-of-the-art performance across diverse simulation and real-world tasks without requiring architectural changes or specialized sensors.
RL-100 is a unified real-world reinforcement learning framework that combines diffusion visuomotor policies with a clipped PPO objective and consistency distillation to achieve 100% success across 1,000 diverse robotic manipulation trials, matching or surpassing human experts while demonstrating robust zero-shot generalization and continuous deployment in dynamic environments.
This paper introduces the NavSpace benchmark to systematically evaluate the spatial intelligence of navigation agents through six task categories and 1,228 trajectory-instruction pairs, revealing limitations in current models and proposing SNav, a new spatially intelligent navigation model that outperforms existing agents on both the benchmark and real robot tests.
This paper introduces Latent Policy Steering (LPS), a method that leverages embodiment-agnostic optical flow to pretrain a World Model on diverse datasets, which is then fine-tuned with limited target-embodiment demonstrations to steer and significantly improve visuomotor policies in low-data regimes.
The paper introduces LLM-Advisor, a prompt-based framework that leverages large language models as non-decisive post-processing advisors to significantly improve the cost efficiency of path planning across diverse terrains without modifying underlying planners, while addressing hallucination risks and demonstrating superior performance over zero-shot LLM approaches.
This paper proposes CoHet, a novel algorithm that leverages Graph Neural Network-driven intrinsic rewards to enable effective decentralized learning and cooperation among heterogeneous multi-agent systems despite challenges like partial observability and reward sparsity, demonstrating superior performance over state-of-the-art methods in standard benchmarks.
This paper introduces BEACON, a language-conditioned navigation system that overcomes the limitations of existing 2D image-space methods by predicting an occlusion-aware Bird's-Eye View affordance heatmap from surround-view RGB-D observations, thereby significantly improving the accuracy of inferring traversable targets in occluded regions.
This paper introduces the Dynamics-Aware Policy Learning (DAPL) framework, which leverages explicit world modeling to learn contact-induced dynamics, enabling robots to achieve robust extrinsic dexterity in cluttered environments without hand-crafted heuristics and significantly outperforming existing manipulation methods in both simulation and real-world deployments.
This paper introduces "Open-World Motion Forecasting," an end-to-end class-incremental framework that predicts future trajectories directly from camera images while mitigating catastrophic forgetting through pseudo-labeling with vision-language models and a novel query feature variance-based replay strategy, enabling continual adaptation to evolving object taxonomies in real-world autonomous driving.