arXiv:2603.25646v1 Announce Type: new
Abstract: This paper presents an experimental platform for studying intentional-state attribution toward a non-humanoid robot. The system combines a simulated robot, realistic task environments, and large language…
arXiv:2602.00678v3 Announce Type: replace
Abstract: Reinforcement learning has shown strong promise for quadrupedal agile locomotion, even with proprioception-only sensing. In practice, however, sim-to-real gap and reward overfitting in complex terrai…
arXiv:2603.25623v1 Announce Type: new
Abstract: Robust scene representation is essential for autonomous systems to safely operate in challenging low-visibility environments. Radar has a clear advantage over cameras and lidars in these conditions due t…
arXiv:2512.00939v2 Announce Type: replace
Abstract: Recent progress in contact-rich robotic manipulation has been striking, yet most deployed systems remain confined to simple, scripted routines. One of the key barriers is the lack of motion planning …
arXiv:2603.25583v1 Announce Type: new
Abstract: The lack of sufficiently diverse data, coupled with limited data efficiency, remains a major bottleneck for generalist robotic models, yet systematic strategies for collecting and curating such data are …
arXiv:2511.14427v2 Announce Type: replace
Abstract: Effective contact-rich manipulation requires robots to synergistically leverage vision, force, and proprioception. However, Reinforcement Learning agents struggle to learn in such multisensory settin…
arXiv:2603.25544v1 Announce Type: new
Abstract: Learning motor control for muscle-driven musculoskeletal models is hindered by the computational cost of biomechanically accurate simulation and the scarcity of validated, open full-body models. Here we …
arXiv:2510.14612v2 Announce Type: replace
Abstract: This paper presents a novel approach for representing proprioceptive time-series data from quadruped robots as structured two-dimensional images, enabling the use of convolutional neural networks for…
arXiv:2603.25481v1 Announce Type: new
Abstract: We address language-conditioned robotic manipulation using flow-based trajectory generation, which enables training on human and web videos of object manipulation and requires only minimal embodiment-spe…
arXiv:2509.08522v3 Announce Type: replace
Abstract: This paper presents RoboMatch, a novel unified teleoperation platform for mobile manipulation with an auto-matching network architecture, designed to tackle long-horizon tasks in dynamic environments…