Author name cluster

Alexander Winkler

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3 papers

2 author rows

NeurIPS Conference 2024 Conference Paper

Omnigrasp: Grasping Diverse Objects with Simulated Humanoids

Zhengyi Luo
Jinkun Cao
Sammy Christen
Alexander Winkler
Kris Kitani
Weipeng Xu

We present a method for controlling a simulated humanoid to grasp an object and move it to follow an object's trajectory. Due to the challenges in controlling a humanoid with dexterous hands, prior methods often use a disembodied hand and only consider vertical lifts or short trajectories. This limited scope hampers their applicability for object manipulation required for animation and simulation. To close this gap, we learn a controller that can pick up a large number (>1200) of objects and carry them to follow randomly generated trajectories. Our key insight is to leverage a humanoid motion representation that provides human-like motor skills and significantly speeds up training. Using only simplistic reward, state, and object representations, our method shows favorable scalability on diverse objects and trajectories. For training, we do not need a dataset of paired full-body motion and object trajectories. At test time, we only require the object mesh and desired trajectories for grasping and transporting. To demonstrate the capabilities of our method, we show state-of-the-art success rates in following object trajectories and generalizing to unseen objects. Code and models will be released.

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ICLR Conference 2024 Conference Paper

Universal Humanoid Motion Representations for Physics-Based Control

Zhengyi Luo 0002
Jinkun Cao
Josh Merel
Alexander Winkler
Jing Huang 0020
Kris Kitani
Weipeng Xu

We present a universal motion representation that encompasses a comprehensive range of motor skills for physics-based humanoid control. Due to the high dimensionality of humanoids and the inherent difficulties in reinforcement learning, prior methods have focused on learning skill embeddings for a narrow range of movement styles (e.g. locomotion, game characters) from specialized motion datasets. This limited scope hampers their applicability in complex tasks. We close this gap by significantly increasing the coverage of our motion representation space. To achieve this, we first learn a motion imitator that can imitate all of human motion from a large, unstructured motion dataset. We then create our motion representation by distilling skills directly from the imitator. This is achieved by using an encoder-decoder structure with a variational information bottleneck. Additionally, we jointly learn a prior conditioned on proprioception (humanoid's own pose and velocities) to improve model expressiveness and sampling efficiency for downstream tasks. By sampling from the prior, we can generate long, stable, and diverse human motions. Using this latent space for hierarchical RL, we show that our policies solve tasks using human-like behavior. We demonstrate the effectiveness of our motion representation by solving generative tasks (e.g. strike, terrain traversal) and motion tracking using VR controllers.

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IROS Conference 2007 Conference Paper

Dynamic force/torque measurement using a 12DOF sensor

Alexander Winkler
Jozef Suchy

This article presents an algorithm for dynamic force/torque measurement and robot load identification using the so called 12DOF sensor to measure forces/torques and linear/angular accelerations. The basic equations of dynamic forces and torques arising during robot motion and acting on the end-effector were worked out. To be able to perform the experiments suitable robot system based on a six axes articulated manipulator was constituted. For this system also the appropriate software was developed. The load parameters of the tool were determined during particular motion sequence of the robot and the values were compared with values from CAD software. The compensation of dynamic forces and torques is verified using the experimental robot system and the results are presented.

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