Zobrazeno 1 - 10
of 77
pro vyhledávání: '"Posa, Michael"'
Autor:
Yang, William, Posa, Michael
Non-prehensile manipulation enables fast interactions with objects by circumventing the need to grasp and ungrasp as well as handling objects that cannot be grasped through force closure. Current approaches to non-prehensile manipulation focus on sta
Externí odkaz:
http://arxiv.org/abs/2405.08731
Robotic manipulation can greatly benefit from the data efficiency, robustness, and predictability of model-based methods if robots can quickly generate models of novel objects they encounter. This is especially difficult when effects like complex joi
Externí odkaz:
http://arxiv.org/abs/2310.12054
The hybrid nature of multi-contact robotic systems, due to making and breaking contact with the environment, creates significant challenges for high-quality control. Existing model-based methods typically rely on either good prior knowledge of the mu
Externí odkaz:
http://arxiv.org/abs/2310.09893
Model-based approaches for planning and control for bipedal locomotion have a long history of success. It can provide stability and safety guarantees while being effective in accomplishing many locomotion tasks. Model-free reinforcement learning, on
Externí odkaz:
http://arxiv.org/abs/2310.09873
Autor:
Bui, Hien, Posa, Michael
In contact-rich tasks, the hybrid, multi-modal nature of contact dynamics poses great challenges in model representation, planning, and control. Recent efforts have attempted to address these challenges via data-driven methods, learning dynamical mod
Externí odkaz:
http://arxiv.org/abs/2310.09714
Autor:
Acosta, Brian, Posa, Michael
Bipedal robots promise the ability to traverse rough terrain quickly and efficiently, and indeed, humanoid robots can now use strong ankles and careful foot placement to traverse discontinuous terrain. However, more agile underactuated bipeds have sm
Externí odkaz:
http://arxiv.org/abs/2309.07993
This work presents an instance-agnostic learning framework that fuses vision with dynamics to simultaneously learn shape, pose trajectories, and physical properties via the use of geometry as a shared representation. Unlike many contact learning appr
Externí odkaz:
http://arxiv.org/abs/2309.05832
We propose a hybrid model predictive control algorithm, consensus complementarity control (C3), for systems that make and break contact with their environment. Many state-of-the-art controllers for tasks which require initiating contact with the envi
Externí odkaz:
http://arxiv.org/abs/2304.11259
Autor:
Yang, William, Posa, Michael
When legged robots impact their environment executing dynamic motions, they undergo large changes in their velocities in a short amount of time. Measuring and applying feedback to these velocities is challenging, further complicated by uncertainty in
Externí odkaz:
http://arxiv.org/abs/2303.00817
Reduced-order models (ROM) are popular in online motion planning due to their simplicity. A good ROM for control captures critical task-relevant aspects of the full dynamics while remaining low dimensional. However, planning within the reduced-order
Externí odkaz:
http://arxiv.org/abs/2301.02075