AMOR: Adaptive Character Control through Multi-Objective Reinforcement Learning
Robot Motion Diffusion Model: Motion Generation for Robotic Characters
We introduce a novel method that integrates kinematic generative models with physics based character control. Our approach begins by training a reward surrogate to predict the performance of the downstream non-differentiable control task, offering an efficient and differentiable loss function.
Soft Pneumatic Actuator Design using Differentiable Simulation
Interactive Design of Stylized Walking Gaits for Robotic Characters
Name Pronunciation Extraction and Reuse in Human-Agent Conversation
We present a pipeline for fusing text and audio features to extract and re-use user information like names with the correct pronunciation.
Optimal Design of Robotic Character Kinematics
In this paper, we propose a technique that simultaneously solves for optimal design and control parameters for a robotic character whose design is parameterized with configurable joints. At the technical core of our technique is an efficient solution strategy that uses dynamic programming to solve for optimal state, control, and design parameters, together with a strategy to remove redundant constraints that commonly exist in general robot assemblies with kinematic loops.
Transformer-based Neural Augmentation of Robot Simulation Representations
We propose to augment common simulation representations with a transformer-inspired architecture, by training a network to predict the true state of robot building blocks given their simulation state. Because we augment building blocks, rather than the full simulation state, we make our approach modular which improves generalizability and robustness.
DOC: Differentiable Optimal Control for Retargeting Motions onto Legged Robots
Legged robots are designed to perform highly dynamic motions. However, it remains challenging for users to retarget expressive motions onto these complex systems. In this paper, we present a Differentiable Optimal Control (DOC) framework that facilitates the transfer of rich motions from either animals or animations onto these robots.
Improving a Robot’s Turn-Taking Behavior in Dynamic Multiparty Interactions
We present ongoing work to develop a robust and natural turn-taking behavior for a social agent to engage a dynamically changing group in a conversation.
A Versatile Inverse Kinematics Formulation for Retargeting Motions onto Robots with Kinematic Loops
Robots with kinematic loops are known to have superior mechanical performance. However, due to these loops, their modeling and control is challenging, and prevents a more widespread use. In this paper, we describe a versatile Inverse Kinematics (IK) formulation for the retargeting of expressive motions onto mechanical systems with loops.
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