Yuquan Wang

Xingzhou Chen, Xiayan Xu, Yan Ning et al.

Humanoid robots are entering our physical world at scale, yet as oversized toys--good at singing and dancing, but short on force-interaction capabilities for practical tasks. Bridging this gap necessitates prioritizing reliable contact perception as a fundamental requirement. Estimating external wrenches in humanoids is complicated by floating-base dynamics and indeterminate contact locations. Existing analytical frameworks require idealistic assumptions and hard-to-obtain measurements, which are often unavailable in practice. To bridge this gap, we propose SixthSense, a task-agnostic approach that infers whole-body contact timing, location, and wrenches from proprioception and IMU data alone. To capture the multi-modal dynamics between unstructured contact inputs and the uncertain motion outputs, we employ conditional flow matching to tokenize proprioceptive histories and estimate a spatiotemporally sparse contact-event flow. SixthSense serves as a plug-and-play perception module for applications including collision detection, physical human-robot interaction, and force-feedback teleoperation. Experiments across standing, walking, and whole-body motion-tracking policies showcased unprecedented performance in diverse behaviors.

Yuquan Wang, Niels Dehio, Abderrahmane Kheddar

In order to enable on-purpose robotic impact tasks, predicting joint-velocity jumps is essential to enforce controller feasibility and hardware integrity. We observe a considerable prediction error of a commonly-used approach in robotics compared against 250 benchmark experiments with the Panda manipulator. We reduce the average prediction error by 81.98% as follows: First, we focus on task-space equations without inverting the ill-conditioned joint-space inertia matrix. Second, before the impact event, we compute the equivalent inertial properties of the end-effector tip considering that a high-gains (stiff) kinematic-controlled manipulator behaves like a composite-rigid body.

Yuquan Wang, Niels Dehio, Abderrahmane Kheddar

State-of-the-art impact dynamics models either apply for free-flying objects or do not account that a robotic manipulator is commonly high-stiffness controlled. Thus, we lack tailor-made models for manipulators mounted on a fixed base. Focusing on orthogonal point-to-surface impacts (no tangential velocities), we revisit two main elements of an impact dynamics model: the contact-force model and the inverse inertia matrix. We collect contact-force measurements by impacting a 7 DOF Panda robot against a sensorized rigid environment with various joint configurations and velocities. Evaluating the measurements from 150 trials, the best model-to-data matching suggests a viscoelastic contact-force model and computing the inverse inertia matrix assuming the robot is a composite-rigid body.

Yuquan Wang, Niels Dehio, Arnaud Tanguy et al.

Robots usually establish contacts at rigid surfaces with near-zero relative velocities. Otherwise, impact-induced energy propagates in the robot's linkage and may cause irreversible damage to the hardware. Moreover, abrupt changes in task-space contact velocity and peak impact forces also result in abrupt changes in robot joint velocities and torques; which can compromise controllers' stability, especially for those based on smooth models. In reality, several tasks would require establishing contact with moderately high velocity. We propose to enhance task-space multi-objective controllers formulated as a quadratic program to be resilient to frictional impacts in three dimensions. We devise new constraints and reformulate the usual ones to be robust to the abrupt joint state changes mentioned earlier. The impact event becomes a controlled process once the optimal control search space is aware of: (1) the hardware-affordable impact bounds and (2) analytically-computed feasible set (polyhedra) that constrain post-impact critical states. Prior to and nearby the targeted contact spot, we assume, at each control cycle, that the impact will occur at the next iteration. This somewhat one-step preview makes our controller robust to impact time and location. To assess our approach, we experimented its resilience to moderate impacts with the Panda manipulator and achieved swift grabbing tasks with the HRP-4 humanoid robot.

Yuquan Wang, Arnaud Tanguy, Pierre Gergondet et al.

Impact-aware tasks (i.e. on purpose impacts) are not handled in multi-objective whole body controllers of hu-manoid robots. This leads to the fact that a humanoid robot typically operates at near-zero velocity to interact with the external environment. We explicitly investigate the propagation of the impact-induced velocity and torque jumps along the structure linkage and propose a set of constraints that always satisfy the hardware limits, sustain already established contacts and the stability measure, i.e. the zero moment point condition. Without assumptions on the impact location or timing, our proposed controller enables humanoid robots to generate non-zero contact velocity without breaking the established contacts or falling. The novelty of our approach lies in building on existing continuous dynamics whole body multi-objective controller without the need of reset-maps or hybrid control.