aiqbench analysis
This research introduces Unified Behavioral Models (UBMs) and their Koopman instantiation to address the data and compute inefficiencies of current diffusion-based dexterity models. By framing skills as coupled dynamical systems, the approach ensures inherent temporal coherence and enables efficient online replanning when visual predictions diverge from observations. The method matches or exceeds state-of-the-art baselines across simulated and real-world tasks while offering faster inference and robustness to occlusions.
Going with the Flow: Koopman Behavioral Models as Pseudo Planners for Visuo-Motor Dexterity
arXiv:2602.07413v3 Announce Type: replace Abstract: Contemporary visuo-motor dexterity models often rely on expressive policy classes with diffusion and transformer backbones to achieve strong performance. However, these architectures require significant data and computational resources, and remain far from reliable, particularly for multi-fingered dexterity. Importantly, they model skills as reactive mappings and rely on fixed-horizon action chunking, creating a rigid trade-off between temporal coherence and reactivity. To address these issues, we first introduce Unified Behavioral Models (UBMs), a framework to represent dexterous skills as coupled dynamical systems that capture how visual features of the environment (visual flow) and proprioceptive states of the robot (action flow) co-evolve. As such, UBMs ensure temporal coherence by construction rather than heuristic averaging. Unlike world models that attempt to predict the impact of arbitrary robot actions on the environment, UBMs target behavioral dynamics that encode how demonstrated robot behavior is related to desired impacts on the environment. A UBM can be viewed as a pseudo planner: given an initial condition, it computes the desired robot behavior over the entire skill horizon, while simultaneously ``imagining" the resulting flow of visual features. To operationalize UBMs, we propose Koopman-UBM, a first instantiation of UBMs as a structured latent linear system. K-UBM is computationally efficient, enabling reactivity and adaptation via an online replanning strategy: the model acts as its own runtime monitor, automatically triggering replanning when predicted and observed visual flow diverge beyond a threshold. Across seven simulated tasks and four real-world tasks, our approach matches or exceeds the performance of state-of-the-art baselines, while offering considerably faster inference, smooth execution, robustness to occlusions, and flexible replanning.
Abstract:Contemporary visuo-motor dexterity models often rely on expressive policy classes with diffusion and transformer backbones to achieve strong performance. However, these architectures require significant data and computational resources, and remain far from reliable, particularly for multi-fingered dexterity. Importantly, they model skills as reactive mappings and rely on fixed-horizon action chunking, creating a rigid trade-off between temporal coherence and reactivity. To address these issues, we first introduce Unified Behavioral Models (UBMs), a framework to represent dexterous skills as coupled dynamical systems that capture how visual features of the environment (visual flow) and proprioceptive states of the robot (action flow) co-evolve. As such, UBMs ensure temporal coherence by construction rather than heuristic averaging. Unlike world models that attempt to predict the impact of arbitrary robot actions on the environment, UBMs target behavioral dynamics that encode how demonstrated robot behavior is related to desired impacts on the environment. A UBM can be viewed as a pseudo planner: given an initial condition, it computes the desired robot behavior over the entire skill horizon, while simultaneously ``imagining" the resulting flow of visual features. To operationalize UBMs, we propose Koopman-UBM, a first instantiation of UBMs as a structured latent linear system. K-UBM is computationally efficient, enabling reactivity and adaptation via an online replanning strategy: the model acts as its own runtime monitor, automatically triggering replanning when predicted and observed visual flow diverge beyond a threshold. Across seven simulated tasks and four real-world tasks, our approach matches or exceeds the performance of state-of-the-art baselines, while offering considerably faster inference, smooth execution, robustness to occlusions, and flexible replanning.
Robotics (cs.RO)
arXiv:2602.07413 [cs.RO]
(or arXiv:2602.07413v3 [cs.RO] for this version)
Full text under the publisher's open licence · 270 words
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