A lightweight primitive classifier and differentiable guidance mechanism improve pretrained diffusion and flow manipulation policies by 3–7 points at test time without retraining.
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5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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FPL trains a language-conditioned reward model from per-axis human preferences and a reward-conditioned policy, reporting 38-point average success gains over sparse-reward and binary-preference baselines on six manipulation tasks.
The paper provides a task-driven benchmark comparing visual, acoustic, magnetic, and resistive tactile sensors on three manipulation tasks and concludes that sensor utility depends on modality, material friction, and task specifics.
DexSynRefine couples HOI motion manifold flow primitives with task-space residual RL and proprioceptive adaptation to convert human-object interaction data into executable dexterous robot motions, reporting 50-70 point real-world success rate gains over kinematic retargeting on five tasks.
Execution guarantee certifies safe regions for IL policies via view synthesis and set invariance so that maximum task success is assured from within those regions even under small execution changes.
citing papers explorer
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PriGo: Test-Time Primitive Guidance to Diffusion and Flow Policies for Adaptive Robotic Manipulation
A lightweight primitive classifier and differentiable guidance mechanism improve pretrained diffusion and flow manipulation policies by 3–7 points at test time without retraining.
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Freeform Preference Learning for Robotic Manipulation
FPL trains a language-conditioned reward model from per-axis human preferences and a reward-conditioned policy, reporting 38-point average success gains over sparse-reward and binary-preference baselines on six manipulation tasks.
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TacO: Benchmarking Tactile Sensors for Object Manipulation
The paper provides a task-driven benchmark comparing visual, acoustic, magnetic, and resistive tactile sensors on three manipulation tasks and concludes that sensor utility depends on modality, material friction, and task specifics.
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DexSynRefine: Synthesizing and Refining Human-Object Interaction Motion for Physically Feasible Dexterous Robot Actions
DexSynRefine couples HOI motion manifold flow primitives with task-space residual RL and proprioceptive adaptation to convert human-object interaction data into executable dexterous robot motions, reporting 50-70 point real-world success rate gains over kinematic retargeting on five tasks.
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To Do or Not to Do: Ensuring the Safety of Visuomotor Policies Learned from Demonstrations
Execution guarantee certifies safe regions for IL policies via view synthesis and set invariance so that maximum task success is assured from within those regions even under small execution changes.