B-spline Policy: Accelerating Manipulation Policies via B-spline Action Representations
- URL: http://arxiv.org/abs/2607.09648v1
- Date: Fri, 10 Jul 2026 17:46:32 GMT
- Title: B-spline Policy: Accelerating Manipulation Policies via B-spline Action Representations
- Abstract summary: B-spline Policy ( BSP) is an action representation designed for accelerating robot manipulation policies.<n> BSP parameterizes actions as continuous B-spline curves defined by a set of knots and control points.
- Score: 75.39816527838181
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this work, we present B-spline Policy (BSP), an action representation designed for accelerating robot manipulation policies. Rather than predicting discrete-time action chunks, BSP parameterizes actions as continuous B-spline curves defined by a set of knots and control points. This representation yields smooth, time-continuous trajectories that can be temporally scaled and executed by low-level controllers at higher frequencies and speeds. We show that B-spline-parameterized actions can be seamlessly integrated into standard policy learning pipelines by directly predicting B-spline parameters. Experiments on simulated and real-world tasks demonstrate that BSP significantly reduces task completion time, achieving substantial improvements over baseline methods while maintaining strong success rates. More results: https://b-spline-policy.github.io
Related papers
- PAWS: Preference Learning with Advantage-Weighted Segments [61.590521943273]
We propose a segment-based preference learning method that performs policy updates directly using segment-level advantage functions.<n>Experiments on simulated robotic manipulation and locomotion tasks demonstrate that PAWS consistently outperforms existing PbRL approaches.
arXiv Detail & Related papers (2026-06-10T12:00:17Z) - Dynamic Execution Horizon Prediction for Chunk-based Robot Policies [29.482987292744568]
Action chunking has become a standard design in modern robot policies, where the policy predicts a sequence of actions and executes a fixed number of them instead of acting one step at a time.<n>In practice, the execution horizon is typically chosen through empirical tuning and is highly task-dependent.<n>We propose Dynamic Execution Horizon Prediction (DEHP), an effective method that trains a lightweight execution-horizon prediction branch using online reinforcement learning.
arXiv Detail & Related papers (2026-06-09T19:58:31Z) - Spline Policy: A Structured Representation for Robot Policies [18.73114596777634]
This paper studies Spline Policy (SP), a structured representation that replaces action chunks with spline parameters while keeping the policy backbone unchanged.<n>The predicted spline can be decoded as a compact continuous trajectory, queried at different temporal resolutions, constrained or edited in parameter space, and passed to downstream controllers.<n> Experiments in low-dimensional motion learning, simulated manipulation under matched backbones, dexterous manipulation, and real-robot case studies show that SP remains compatible with modern policy learners.
arXiv Detail & Related papers (2026-06-05T15:23:54Z) - OGPO: Sample Efficient Full-Finetuning of Generative Control Policies [53.42266064673132]
Generative control policies (GCPs) have emerged as effective parameterizations for robot learning.<n>This work introduces Off-policy Generative Policy Optimization (OGPO), a sample-efficient for finetuning GCPs.<n>OGPO achieves state-of-the-art performance on manipulation tasks spanning multi-task settings, high-precision insertion, and dexterous control.
arXiv Detail & Related papers (2026-05-04T18:36:40Z) - Speedup Patch: Learning a Plug-and-Play Policy to Accelerate Embodied Manipulation [19.765462206627955]
Speedup Patch (SuP) is a policy-agnostic framework that enables plug-and-play acceleration using solely offline data.<n>SuP achieves an overall 1.8x execution speedup for diverse policies while maintaining their original success rates.
arXiv Detail & Related papers (2026-03-21T05:29:04Z) - ABPolicy: Asynchronous B-Spline Flow Policy for Real-Time and Smooth Robotic Manipulation [17.98625177131488]
ABPolicy is a flow-matching policy that operates in a B-spline control-point action space.<n>By leveraging asynchronous inference, ABPolicy delivers real-time, continuous updates.<n> Empirical results indicate that ABPolicy reduces trajectory jerk, leading to smoother motion and improved performance.
arXiv Detail & Related papers (2026-02-27T10:50:13Z) - HPTune: Hierarchical Proactive Tuning for Collision-Free Model Predictive Control [55.015741360766945]
We propose a hierarchical proactive tuning (HPTune) framework that combines both a fast-level tuning and a slow-level tuning.<n>We integrate HPTune with the Doppler LiDAR that provides obstacle velocities apart from position-only measurements for enhanced motion predictions.<n>It is found that HPTune enables situation-tailored motion planning by formulating a safe, agile collision avoidance strategy.
arXiv Detail & Related papers (2026-01-29T07:15:39Z) - SpeedAug: Policy Acceleration via Tempo-Enriched Policy and RL Fine-Tuning [52.29534291796025]
Reinforcement learning is a promising approach that adapts policies for faster execution without additional demonstration.<n>We propose SpeedAug, an RL-based policy acceleration framework that efficiently adapts pre-trained policies for faster task execution.
arXiv Detail & Related papers (2025-11-24T04:25:47Z) - Improving Generative Behavior Cloning via Self-Guidance and Adaptive Chunking [29.920087317401396]
Generative Behavior Cloning is a simple yet effective framework for robot learning.<n>We propose two novel techniques to enhance the consistency and reactivity of diffusion policies.<n>Our approach substantially improves GBC performance across a wide range of simulated and real-world robotic manipulation tasks.
arXiv Detail & Related papers (2025-10-14T11:16:34Z) - Relative Entropy Pathwise Policy Optimization [66.03329137921949]
We present an on-policy algorithm that trains Q-value models purely from on-policy trajectories.<n>We show how to combine policies for exploration with constrained updates for stable training, and evaluate important architectural components that stabilize value function learning.
arXiv Detail & Related papers (2025-07-15T06:24:07Z) - Bidirectional Decoding: Improving Action Chunking via Guided Test-Time Sampling [51.38330727868982]
We show how action chunking impacts the divergence between a learner and a demonstrator.<n>We propose Bidirectional Decoding (BID), a test-time inference algorithm that bridges action chunking with closed-loop adaptation.<n>Our method boosts the performance of two state-of-the-art generative policies across seven simulation benchmarks and two real-world tasks.
arXiv Detail & Related papers (2024-08-30T15:39:34Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.