Zobrazeno 1 - 10
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pro vyhledávání: '"Sentís A"'
Cross-embodiment imitation learning enables policies trained on specific embodiments to transfer across different robots, unlocking the potential for large-scale imitation learning that is both cost-effective and highly reusable. This paper presents
Externí odkaz:
http://arxiv.org/abs/2411.03682
Wildfire suppression is a complex task that poses high risks to humans. Using robotic teams for wildfire suppression enhances the safety and efficiency of detecting, monitoring, and extinguishing fires. We propose a control architecture based on task
Externí odkaz:
http://arxiv.org/abs/2410.08602
Autor:
Gonzalez, Carlos, Sentis, Luis
Humanoid robots rely on multi-contact planners to navigate a diverse set of environments, including those that are unstructured and highly constrained. To synthesize stable multi-contact plans within a reasonable time frame, most planners assume stat
Externí odkaz:
http://arxiv.org/abs/2410.08335
Autor:
Bang, Seung Hyeon, Gonzalez, Carlos, Moore, Gabriel, Kang, Dong Ho, Seo, Mingyo, Sentis, Luis
This paper presents an open-source, lightweight, yet comprehensive software framework, named RPC, which integrates physics-based simulators, planning and control libraries, debugging tools, and a user-friendly operator interface. RPC enables users to
Externí odkaz:
http://arxiv.org/abs/2409.10015
This paper presents a set of simple and intuitive robot collision detection algorithms that show substantial scaling improvements for high geometric complexity and large numbers of collision queries by leveraging hardware-accelerated ray tracing on G
Externí odkaz:
http://arxiv.org/abs/2409.09918
Multi-suction-cup grippers are frequently employed to perform pick-and-place robotic tasks, especially in industrial settings where grasping a wide range of light to heavy objects in limited amounts of time is a common requirement. However, most exis
Externí odkaz:
http://arxiv.org/abs/2408.03498
This paper proposes an online bipedal footstep planning strategy that combines model predictive control (MPC) and reinforcement learning (RL) to achieve agile and robust bipedal maneuvers. While MPC-based foot placement controllers have demonstrated
Externí odkaz:
http://arxiv.org/abs/2407.17683
This paper proposes a novel control framework for agile and robust bipedal locomotion, addressing model discrepancies between full-body and reduced-order models. Specifically, assumptions such as constant centroidal inertia have introduced significan
Externí odkaz:
http://arxiv.org/abs/2407.16811
Autor:
Llorens, Santiago, González, Walther, Sentís, Gael, Calsamiglia, John, Bagan, Emili, Muñoz-Tapia, Ramon
This paper introduces quantum edge detection, aimed at locating boundaries of quantum domains where all particles share the same pure state. Focusing on the 1D scenario of a string of particles, we develop an optimal protocol for quantum edge detecti
Externí odkaz:
http://arxiv.org/abs/2405.11373
Jerk-constrained trajectories offer a wide range of advantages that collectively improve the performance of robotic systems, including increased energy efficiency, durability, and safety. In this paper, we present a novel approach to jerk-constrained
Externí odkaz:
http://arxiv.org/abs/2404.07889