相关论文: Proprioceptive External Torque Learning for Floati…

Over the past decades, we have witnessed a rapid emergence of soft and reconfigurable robots thanks to their capability to interact safely with humans and adapt to complex environments. However, their softness makes accurate control very…

In this paper, we present algorithms to estimate external contact forces and joint torques using only skin, i.e. distributed tactile sensors. To deal with gaps between the tactile sensors (taxels), we use interpolation techniques. The…

Retrieving rich contact information from robotic tactile sensing has been a challenging, yet significant task for the effective perception of object properties that the robot interacts with. This work is dedicated to developing an algorithm…

Various robots have been developed so far; however, we face challenges in modeling the low-rigidity bodies of some robots. In particular, the deflection of the body changes during tool-use due to object grasping, resulting in significant…

This paper proposes and validates an in situ calibration method to calibrate six axis force torque (F/T) sensors once they are mounted on the system. This procedure takes advantage of the knowledge of the model of the robot to generate the…

We propose a novel method, ProNav, which uses proprioceptive signals for traversability estimation in challenging outdoor terrains for autonomous legged robot navigation. Our approach uses sensor data from a legged robot's joint encoders,…

The online adaptation of exoskeleton control based on muscle activity sensing offers a promising approach to personalizing exoskeleton behavior based on the user's biosignals. While electromyography (EMG)-based methods have demonstrated…

Force and torque (F/T) sensing is critical for robot-environment interaction, but physical F/T sensors impose constraints in size, cost, and fragility. To mitigate this, recent studies have estimated force/wrench sensorlessly from robot…

In order to make robots more useful in a variety of environments, they need to be highly portable so that they can be transported to wherever they are needed, and highly storable so that they can be stored when not in use. We propose…

Legged robots that can operate autonomously in remote and hazardous environments will greatly increase opportunities for exploration into under-explored areas. Exteroceptive perception is crucial for fast and energy-efficient locomotion:…

Force and torque sensing is crucial in robotic manipulation across both collaborative and industrial settings. Traditional methods for dynamics identification enable the detection and control of external forces and torques without the need…

We propose a method that simultaneously estimates and controls extrinsic contact with tactile feedback. The method enables challenging manipulation tasks that require controlling light forces and accurate motions in contact, such as…

General robot manipulation requires the handling of previously unseen objects. Learning a physically accurate model at test time can provide significant benefits in data efficiency, predictability, and reuse between tasks. Tactile sensing…

Legged robots can traverse a wide variety of terrains, some of which may be challenging for wheeled robots, such as stairs or highly uneven surfaces. However, quadruped robots face stability challenges on slippery surfaces. This can be…

Measurement of environment interaction forces during robotic minimally-invasive surgery would enable haptic feedback to the surgeon, thereby solving one long-standing limitation. Estimating this force from existing sensor data avoids the…

Dexterous manipulation requires careful reasoning over extrinsic contacts. The prevalence of deforming tools in human environments, the use of deformable sensors, and the increasing number of soft robots yields a need for approaches that…

Accurate inertial parameter identification is crucial for the simulation and control of robots encountering intermittent contact with the environment. Classically, robots' inertial parameters are obtained from CAD models that are not…

Sim-to-real reinforcement learning (RL) for humanoid robots with high-gear ratio actuators remains challenging due to complex actuator dynamics and the absence of torque sensors. To address this, we propose a novel RL framework leveraging…

Humanoid and legged robots interact with the environment through intermittent contacts, making accurate motion estimation fundamentally dependent on reasoning about contact dynamics. However, standard sensing pipelines-whether based on…

Detecting and localizing contacts is essential for robot manipulators to perform contact-rich tasks in unstructured environments. While robot skins can localize contacts on the surface of robot arms, these sensors are not yet robust or…