Motor Control and Adaptation

Latest papers

• No Plan, Yet Human: A Reactive Robotics Model Predicts Human Planning Failures on a Clinical Task

  Michael Migacev, Vito Mengers, Antonia K\"ongeter, Oliver Brock · 28 August 2026

  Understanding why some sequential planning problems are harder than others requires models that go beyond average performance. They should capture the specific pattern of which problems are hard, and ideally fail in the same way people do when planning capacity is reduced. We apply AICON, a reactive…

• EgoPHI: Estimating Contact and Force from Egocentric Vision

  Andela Ilic, Rachel Schuchert, Yijing Jiang, Christian Holz · 14 August 2026

  Understanding hand-object interaction from egocentric vision is essential for modeling how people physically engage with the surrounding world. Yet reasoning about physically grounded interaction requires estimating the forces acting on hands and objects, beyond localizing contact. We present EgoPHI…

• Biomechanics-aware Multi-view Markerless Motion Capture of Dexterous Hand Movements

  Pouyan Firouzabadi, J. D. Peiffer, Kunal Shah, Anton Sobinov, Lee E. Miller, R. James Cotton, Wendy M. Murray · 6 July 2026

  Markerless motion capture (MMC) techniques have been widely beneficial in biomechanical analysis of human movement; however, application to complex motions of the hand lags other musculoskeletal systems. The primary goal of this study was to evaluate the performance of a biomechanical reconstruction…

• A Gravitational Interpretation of Fine-Tuning Reversion

  Samuele Poppi, Nils Lukas · 30 June 2026

  Fine-tuning on harmless data can partially undo behaviors acquired earlier in training. Safety can erode under benign post-alignment updates, unlearned capabilities can re-emerge, latent traits can transfer through apparently unrelated supervision, and related post-alignment fragility appears in oth…

• Beyond Single-Source Cognitive Taskonomy:Multi-Source Task Relations through fMRI Transfer Learning

  Junfeng Xia, Wendu Li, Mengjiao Zhang, Jie Guo · 26 June 2026

  Cognitive tasks are organized by shared and specialized neural processes. Masked fMRI reconstruction provides a common self-supervised objective for quantifying transfer relations among task states, but existing reconstruction-based taskonomies mainly study one-to-one transfer from a single source t…

• RoboNaldo: Accurate, Stable and Powerful Humanoid Soccer Shooting via Motion-Guided Curriculum Reinforcement Learning

  Yichao Zhong, Yidan Lu, Yuhang Lu, Tianyang Tang, Haoguang Mai, Yixuan Pan, Tianyu Li, Li Chen, Jingbo Wang, Zhongyu Li, Peng Lu, Hongyang Li · 10 June 2026

  Elite humanoid soccer shooting requires whole-body stability, high-impulse whole-body interactions, and accuracy to targets. Motion tracking-driven reinforcement learning (RL) provides stability in whole-body movement coordination, but a fixed reference makes it hard to adapt to varied ball position…

• From Performance to Viability: A Bootstrap Framework for Latent-Space Representation Learning in Adaptive Biological Systems

  Jacques Raynal, Pierre Slangen, Elsa Raynal, Jacques Margerit · 2 June 2026

  Observable performance is commonly used to characterize biological systems. In adaptive systems, however, similar performances may arise from distinct organizations, and configurations that appear comparable at a given time may follow different longitudinal trajectories. This limitation motivates a …

• Can Predicted Dynamics Exist in the Physical World?

  Barak Or · 2 June 2026

  Predictive Physical AI systems output state rollouts, action chunks, and latent plans, yet a low root-mean-square error (RMSE) does not imply that a particular proposal is physically executable. We formulate physical admissibility as a prediction-control interface: before execution, a decoded propos…

• Observable Performance Does Not Fully Reflect System Organization: A Multi-Level Analysis of Gait Dynamics Under Occlusal Constraint

  Jacques Raynal, Pierre Slangen, Jacques Margerit · 4 May 2026

  In biomechanical systems, observable performance is often used as a proxy for underlying system organization. However, this assumption implicitly presumes a correspondence between output metrics and internal system states that may not hold in adaptive systems. In this study, the vertical dimension o…

• How Fast Should a Model Commit to Supervision? Training Reasoning Models on the Tsallis Loss Continuum

  Chu-Cheng Lin, Eugene Ie · 29 April 2026

  Adapting reasoning models to new tasks during post-training with only output-level supervision stalls under reinforcement learning from verifiable rewards (RLVR) when the initial success probability $p_0$ is small. Using the Tsallis $q$-logarithm, we define a loss family $J_Q$ that interpolates betw…

• (How) Learning Rates Regulate Catastrophic Overtraining

  Mark Rofin, Aditya Varre, Nicolas Flammarion · 16 April 2026

  Supervised fine-tuning (SFT) is a common first stage of LLM post-training, teaching the model to follow instructions and shaping its behavior as a helpful assistant. At the same time, SFT may harm the fundamental capabilities of an LLM, particularly after long pretraining: a phenomenon known as cata…

• Toward Global Intent Inference for Human Motion by Inverse Reinforcement Learning

  Sarmad Mehrdad, Maxime Sabbah, Vincent Bonnet, Ludovic Righetti · 10 March 2026

  This paper investigates whether a single, unified cost function can explain and predict human reaching movements, in contrast with existing approaches that rely on subject- or posture-specific optimization criteria. Using the Minimal Observation Inverse Reinforcement Learning (MO-IRL) algorithm, tog…

• Interaction-Aware Whole-Body Control for Compliant Object Transport

  Hao Zhang, Yves Tseng, Ding Zhao, H. Eric Tseng · 5 March 2026

  Cooperative object transport in unstructured environments remains challenging for assistive humanoids because strong, time-varying interaction forces can make tracking-centric whole-body control unreliable, especially in close-contact support tasks. This paper proposes a bio-inspired, interaction-or…

• Cause-effect perception in an object place task

  Nikolai Bahr, Christoph Zetzsche, Jaime Maldonado, Kerstin Schill · 15 January 2026

  We conducted an exploratory study in virtual reality to examine if people can discover causal relations in a realistic sensorimotor context and how such learning is represented at different processing levels (conscious-cognitive vs. sensorimotor). Additionally, we explored the relation between human…

• Goal Reaching with Eikonal-Constrained Hierarchical Quasimetric Reinforcement Learning

  Vittorio Giammarino, Ahmed H. Qureshi · 16 December 2025

  Goal-Conditioned Reinforcement Learning (GCRL) mitigates the difficulty of reward design by framing tasks as goal reaching rather than maximizing hand-crafted reward signals. In this setting, the optimal goal-conditioned value function naturally forms a quasimetric, motivating Quasimetric RL (QRL), …

• Using reinforcement learning to probe the role of feedback in skill acquisition

  Antonio Terpin, Raffaello D'Andrea · 10 December 2025

  Many high-performance human activities are executed with little or no external feedback: think of a figure skater landing a triple jump, a pitcher throwing a curveball for a strike, or a barista pouring latte art. To study the process of skill acquisition under fully controlled conditions, we bypass…

• Massively Parallel Imitation Learning of Mouse Forelimb Musculoskeletal Reaching Dynamics

  Eric Leonardis, Akira Nagamori, Ayesha Thanawalla, Yuanjia Yang, Joshua Park, Hutton Saunders, Eiman Azim, Talmo Pereira · 1 December 2025

  The brain has evolved to effectively control the body, and in order to understand the relationship we need to model the sensorimotor transformations underlying embodied control. As part of a coordinated effort, we are developing a general-purpose platform for behavior-driven simulation modeling high…