Related papers: Learning Variable Compliance Control From a Few De…
This paper comprehensively surveys research trends in imitation learning for contact-rich robotic tasks. Contact-rich tasks, which require complex physical interactions with the environment, represent a central challenge in robotics due to…
Soft robots have the potential to revolutionize the use of robotic systems with their capability of establishing safe, robust, and adaptable interactions with their environment, but their precise control remains challenging. In contrast,…
Highly constrained manipulation tasks continue to be challenging for autonomous robots as they require high levels of precision, typically less than 1mm, which is often incompatible with what can be achieved by traditional perception…
The design of physical compliance -- its location, degree, and structure -- affects robot performance and robustness in contact-rich tasks. While compliance is often used in the robot's joints, flange, or end-effector, this paper proposes…
Compliance control is essential for safe physical interaction, yet its adoption is limited by hardware requirements such as force torque sensors. While recent reinforcement learning approaches aim to bypass these constraints, they often…
This paper delves into various robotic manipulation control methods designed for dynamic contact tooling operations on a robotic repair platform. The explored control strategies include hybrid position-force control, admittance control,…
We present a hierarchical policy-learning framework that enables a legged humanoid to cooperatively carry extended loads with a human partner using only haptic cues for intent inference. At the upper tier, a lightweight behavior-cloning…
Local-remote systems allow robots to execute complex tasks in hazardous environments such as space and nuclear power stations. However, establishing accurate positional mapping between local and remote devices can be difficult due to time…
The ability of a soft robot to perform specific tasks is determined by its contact configuration, and transitioning between configurations is often necessary to reach a desired position or manipulate an object. Based on this observation, we…
Constraint-based control approaches offer a flexible way to specify robotic manipulation tasks and execute them on robots with many degrees of freedom. However, the specification of task constraints and their associated priorities usually…
This study addresses the challenge of low dexterity in teleoperation tasks caused by limited sensory feedback and visual occlusion. We propose a novel approach that integrates haptic feedback into teleoperation using the adaptive triggers…
Contact-rich manipulation tasks such as precision assembly require precise control of interaction forces, yet existing imitation learning methods rely mainly on vision-only demonstrations. We propose ManipForce, a handheld system designed…
Magnetically actuated robots provide a promising untethered platform for navigation in confined environments, enabling biological studies and targeted micro-delivery. However, dexterous manipulation in complex structures remains…
Robotic skill learning has been increasingly studied but the demonstration collections are more challenging compared to collecting images/videos in computer vision and texts in natural language processing. This paper presents a skill…
Dexterous multi-fingered hands can provide robots with the ability to flexibly perform a wide range of manipulation skills. However, many of the more complex behaviors are also notoriously difficult to control: Performing in-hand object…
Medical robotics can help improve and extend the reach of healthcare services. A major challenge for medical robots is the complex physical interaction between the robot and the patients which is required to be safe. This work presents the…
Force interaction is inevitable when robots face multiple operation scenarios. How to make the robot competent in force control for generalized operations such as multi-tasks still remains a challenging problem. Aiming at the…
Robotic loco-manipulation tasks often involve contact-rich interactions with the environment, requiring the joint modeling of contact force and robot position. However, recent visuomotor policies often focus solely on learning position or…
We propose a CompliantVLA-adaptor that augments the state-of-the-art Vision-Language-Action (VLA) models with vision-language model (VLM)-informed context-aware variable impedance control (VIC) to improve the safety and effectiveness of…
The design of feedback controllers for bipedal robots is challenging due to the hybrid nature of its dynamics and the complexity imposed by high-dimensional bipedal models. In this paper, we present a novel approach for the design of…