🦾 Category

cs.RO

690 papers

Aero-Promptness: Drag-Aware Aerodynamic Manipulability for Propeller-driven Vehicles

This paper introduces Drag-Aware Aerodynamic Manipulability (DAAM), a geometric framework for control allocation in redundant multirotors that utilizes a Riemannian metric to explicitly account for motor torque limits and aerodynamic drag, thereby generating a state-dependent manipulability volume that serves as a natural barrier function to optimize redundancy resolution while characterizing the resulting smooth manifolds and global jump discontinuities.

Antonio FranchiTue, 10 Ma🔢 math

Dual-Horizon Hybrid Internal Model for Low-Gravity Quadrupedal Jumping with Hardware-in-the-Loop Validation

This paper introduces a Dual-Horizon Hybrid Internal Model that enables stable, continuous quadrupedal jumping under lunar gravity using only proprioceptive sensing, validated through the MATRIX hardware-in-the-loop testbed which emulates reduced gravity and lunar terrain in real time.

Haozhe Xu, Yifei Zhao, Wenhao Feng, Zhipeng Wang, Hongrui Sang, Cheng Cheng, Xiuxian Li, Zhen Yin, Bin HeTue, 10 Ma💻 cs

Vector Field Augmented Differentiable Policy Learning for Vision-Based Drone Racing

This paper introduces DiffRacing, a novel framework that enhances differentiable policy learning for vision-based drone racing by integrating vector fields to provide stable gradient signals for balancing high-speed gate traversal with obstacle avoidance, while employing a differentiable Delta Action Model to enable robust sim-to-real transfer without explicit system identification.

Yang Su, Feng Yu, Yu Hu, Xinze Niu, Linzuo Zhang, Fangyu Sun, Danping ZouTue, 10 Ma💻 cs

Adaptive Vision-Based Control of Redundant Robots with Null-Space Interaction for Human-Robot Collaboration

This paper proposes a novel adaptive vision-based control scheme with null-space interaction for redundant robots that ensures stable, safe, and effective human-robot collaboration in unknown environments by decoupling primary task execution from interactive adjustments, as validated through augmented reality experiments and Lyapunov stability analysis.

Xiangjie Yan, Chen Chen, Xiang LiTue, 10 Ma💻 cs

DeReCo: Decoupling Representation and Coordination Learning for Object-Adaptive Decentralized Multi-Robot Cooperative Transport

This paper introduces DeReCo, a novel multi-agent reinforcement learning framework that decouples representation and coordination learning through a three-stage training strategy to overcome bidirectional interference, thereby enabling sample-efficient and robust decentralized cooperative transport across objects with diverse shapes and physical properties.

Kazuki Shibata, Ryosuke Sota, Shandil Dhiresh Bosch, Yuki Kadokawa, Tsurumine Yoshihisa, Takamitsu MatsubaraTue, 10 Ma💻 cs

Towards Human-Like Manipulation through RL-Augmented Teleoperation and Mixture-of-Dexterous-Experts VLA

This paper proposes an integrated framework combining RL-augmented teleoperation via the IMCopilot assistant and a Mixture-of-Dexterous-Experts VLA (MoDE-VLA) architecture to overcome data and learning bottlenecks, enabling robust human-like, contact-rich bimanual in-hand manipulation with significantly improved success rates.

Tutian Tang, Xingyu Ji, Wanli Xing, Ce Hao, Wenqiang Xu, Lin Shao, Cewu Lu, Qiaojun Yu, Jiangmiao Pang, Kaifeng ZhangTue, 10 Ma💻 cs

SaiVLA-0: Cerebrum--Pons--Cerebellum Tripartite Architecture for Compute-Aware Vision-Language-Action

SaiVLA-0 introduces a neuroscience-inspired, compute-aware Vision-Language-Action framework featuring a tripartite Cerebrum-Pons-Cerebellum architecture that decouples high-level semantics from real-time control to achieve modular scalability, active foveated vision, and significant improvements in training efficiency and task success rates.

Xiang Shi, Wenlong Huang, Menglin Zou, Xinhai SunTue, 10 Ma🤖 cs.LG

TRIAGE: Type-Routed Interventions via Aleatoric-Epistemic Gated Estimation in Robotic Manipulation and Adaptive Perception -- Don't Treat All Uncertainty the Same

The paper introduces TRIAGE, a lightweight post-hoc framework that decomposes uncertainty into aleatoric and epistemic components to trigger distinct corrective actions—observation recovery for corrupted data and control moderation for model mismatch—thereby significantly improving robotic manipulation success rates and enabling efficient adaptive perception.

Divake Kumar, Sina Tayebati, Devashri Naik, Patrick Poggi, Amanda Sofie Rios, Nilesh Ahuja, Amit Ranjan TrivediTue, 10 Ma🤖 cs.LG

UniGround: Universal 3D Visual Grounding via Training-Free Scene Parsing

UniGround introduces a novel, training-free framework for universal 3D visual grounding that leverages global candidate filtering and local precision reasoning to achieve state-of-the-art zero-shot performance in localizing arbitrary objects within complex 3D environments without relying on pre-trained models or 3D supervision.

Jiaxi Zhang, Yunheng Wang, Wei Lu, Taowen Wang, Weisheng Xu, Shuning Zhang, Yixiao Feng, Yuetong Fang, Renjing XuTue, 10 Ma💻 cs

POIROT: Investigating Direct Tangible vs. Digitally Mediated Interaction and Attitude Moderation in Multi-party Murder Mystery Games

This study challenges the assumption that physical robot interaction universally enhances user experience by demonstrating that while tangible delivery does not inherently improve engagement, it significantly reduces narrative immersion for individuals with high negative attitudes toward robots, who instead benefit from digitally mediated interfaces as a social buffer.

Wen Chen, Rongxi Chen, Shankai Chen, Huiyang Gong, Minghui Guo, Yingri Xu, Xintong Wu, Xinyi FuTue, 10 Ma💻 cs

Multifingered force-aware control for humanoid robots

This paper presents a model-based control framework for humanoid robots that utilizes trained tactile force estimators to dynamically redistribute forces across the torso, arm, wrist, and fingers, thereby maintaining stable contact with objects of varying mass or unstable configurations by minimizing the distance between the Center of Pressure and the contact polygon centroid.

Pasquale Marra, Gabriele M. Caddeo, Ugo Pattacini, Lorenzo NataleTue, 10 Ma💻 cs

Seed2Scale: A Self-Evolving Data Engine for Embodied AI via Small to Large Model Synergy and Multimodal Evaluation

Seed2Scale is a self-evolving data engine that overcomes data bottlenecks in embodied AI by synergizing a lightweight "SuperTiny" model for robust data collection with a large Vision-Language Model for autonomous quality verification, enabling a target model to achieve a 131.2% performance improvement starting from just four seed demonstrations.

Cong Tai, Zhaoyu Zheng, Haixu Long, Hansheng Wu, Zhengbin Long, Haodong Xiang, Rong Shi, Zhuo Cui, Shizhuang Zhang, Gang Qiu, He Wang, Ruifeng Li, Biao Liu, Zhenzhe Sun, Tao ShenTue, 10 Ma💻 cs