Current research in robotics is increasingly focused on enhancing the adaptability and efficiency of robotic systems in dynamic environments. Recent work on force-adaptive reinforcement learning frameworks is enabling humanoid robots to maintain balance and manipulate objects under varying external forces, which is crucial for real-world applications. Simultaneously, advancements in motion generation are addressing the challenges of high-dimensional robots, allowing for safe and efficient trajectory planning that significantly outperforms traditional methods. The integration of visual-language-action models is also gaining traction, facilitating multi-task learning and skill acquisition without the need for extensive retraining. Moreover, innovations in teleoperation, such as lightweight haptic feedback gloves, are improving the quality of human-robot interactions, which is vital for tasks requiring dexterity. These developments collectively aim to solve commercial problems in areas like manufacturing, logistics, and autonomous navigation, making robots more capable and versatile in complex, real-world scenarios.
Maintaining balance under external hand forces is critical for humanoid bimanual manipulation, where interaction forces propagate through the kinematic chain and constrain the feasible manipulation en...
Humanoid robots have achieved significant progress in motion generation and control, exhibiting movements that appear increasingly natural and human-like. Inspired by the Turing Test, we propose the M...
Event cameras offer high temporal resolution and low latency, making them ideal sensors for high-speed robotic applications where conventional cameras suffer from image degradations such as motion blu...
Rigid bodies constitute the smallest manipulable elements in the real world, and understanding how they physically interact is fundamental to embodied reasoning and robotic manipulation. Thus, accurat...
Programming robots by demonstration (PbD) is an intuitive concept, but scaling it to real-world variability remains a challenge for most current teaching frameworks. Conditional task graphs are very e...
Vision-Language-Action (VLA) models excel in static manipulation but struggle in dynamic environments with moving targets. This performance gap primarily stems from a scarcity of dynamic manipulation ...
Safe navigation of autonomous robots remains one of the core challenges in the field, especially in dynamic and uncertain environments. One of the prevalent approaches is safety filtering based on con...
Cloth manipulation is a ubiquitous task in everyday life, but it remains an open challenge for robotics. The difficulties in developing cloth manipulation policies are attributed to the high-dimension...
This paper investigates humanoid whole-body dexterous manipulation, where the efficient collection of high-quality demonstration data remains a central bottleneck. Existing teleoperation systems oft...
We introduce the challenging problem of multi-object system identification from videos, for which prior methods are ill-suited due to their focus on single-object scenes or discrete material classific...