Imitation Dynamics
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Related Articles from SNS
From Discrete to Continuous Highest-earning Imitation Dynamics
arXiv:2605.09831v2 Announce Type: replace Abstract: Imitating the highest earners is a common decision-making heuristic, but in finite populations it can generate persistent fluctuations between strategies. This paper studies whether such fluctuations persist as population size grows in heterogeneous two-strategy populations. We show that the Markov chains describing the discrete imitation dynamics form generalized stochastic approximation processes for a good upper semicontinuous...
From Discrete to Continuous Highest-earning Imitation Dynamics
arXiv:2605.09831v2 Announce Type: replace-cross Abstract: Imitating the highest earners is a common decision-making heuristic, but in finite populations it can generate persistent fluctuations between strategies. This paper studies whether such fluctuations persist as population size grows in heterogeneous two-strategy populations. We show that the Markov chains describing the discrete imitation dynamics form generalized stochastic approximation processes for a good upper semicontinuous...
RCM-ACT: Imitation Learning with Dynamic RCM Calibration for Autonomous Intraocular Foreign Body Removal
arXiv:2508.19191v3 Announce Type: replace Abstract: Intraocular foreign body removal demands millimeter-level precision in confined intraocular spaces, yet existing robotic systems predominantly rely on manual teleoperation with steep learning curves. To address the challenges of autonomous manipulation, particularly kinematic uncertainties from variable motion scaling and Remote Center of Motion (RCM) point variation, we propose RCM-ACT, an imitation learning framework for autonomous...
Symskill: Symbol and Skill Co-Invention for Data-Efficient and Reactive Long-Horizon Manipulation
arXiv:2510.01661v3 Announce Type: replace Abstract: Multi-step manipulation in dynamic environments remains challenging. Imitation learning (IL) is reactive but lacks compositional generalization, since monolithic policies do not decide which skill to reuse when scenes change. Classical task-and-motion planning (TAMP) offers compositionality, but its high planning latency prevents real-time failure recovery.
LDA-1B: Scaling Latent Dynamics Action Model via Universal Embodied Data Ingestion
Announce Type: replace Abstract: Recent robot foundation models largely rely on large-scale behavior cloning, which imitates expert actions but discards transferable dynamics knowledge embedded in heterogeneous embodied data. While the Unified World Model (UWM) formulation has the potential to leverage such diverse data, existing instantiations struggle to scale to foundation-level due to coarse data usage and fragmented datasets.
A study on a Real-Time VR-Based Teleoperation Framework for Manipulator in Dynamic Environment
Announce Type: new Abstract: Robot teleoperation enables safe, non-contact task execution in hazardous environments where direct human access is difficult, and its application has expanded with recent VR technologies. Many VR teleoperation studies, however, have primarily served as data-collection tools for robot imitation learning, so they often do not explicitly address dynamic obstacles, workspace changes, or collision risks during operation. For real deployment aimed at operator safety,...
Expanding Spatial and Temporal Context for Robotic Imitation Learning With Scene Graphs
Announce Type: new Abstract: Imitation learning enables robots to learn how to execute tasks via observation. However, real-world environments like homes and offices are often severely partially observed due to their large spatial scales. In addition, many tasks involve executing a series of subtasks requiring autonomous robots to reason over extended time horizons.
Expanding Spatial and Temporal Context for Robotic Imitation Learning With Scene Graphs
arXiv:2606.01072v2 Announce Type: replace Abstract: Imitation learning enables robots to learn how to execute tasks via observation. However, real-world environments like homes and offices are often severely partially observed due to their large spatial scales. In addition, many tasks involve executing a series of subtasks requiring autonomous robots to reason over extended time horizons.
Surface Constraint Policy for Learning Surface-Constrained and Dynamically Feasible Robot Skills
arXiv:2605.31321v1 Announce Type: new Abstract: Diffusion-based imitation learning methods have driven rapid progress in robot dexterous manipulation tasks. However, they have limitations when applied to tasks that involve complex free-form surface constraints because of their lack of explicit surface geometry constraint modeling and the dynamic feasibility issue, resulting in stochastic action generation that fails to achieve reliable surface alignment and maintain stable contact. To...
Meet Argus: The sea-urchin robot with 20 eyes and legs
Most robots are built to look like something. Engineers designing machines to navigate the real world have, for decades, reached for the same reference points: the human skeleton, the dog's four-legged trot, the insect's crawl. These biological templates have produced impressive machines, but they carry an embedded assumption that a robot needs a front, a back, and a preferred direction of travel.