[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82446-en":3,"doc-seo-82446-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82446,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","B-spline Policy Accelerating Manipulation Policies via B-spline Action Representations","B-spline Policy (BSP) introduces a smooth continuous action representation for accelerating robot manipulation policies. Instead of predicting discrete action chunks, BSP parameterizes actions as B-spline curves using knots and control points, producing time-continuous trajectories that can be temporally scaled for faster execution by low-level controllers. The method integrates into standard policy learning pipelines by directly predicting B-spline parameters, with experiments across simulation and real-world tasks showing reduced completion time and maintained strong success rates.","arXiv :2607 .09648v1 [ cs .RO] 10 Jul 2026  \nB-spline Policy: Accelerating Manipulation Policies via B-spline Action Representations  \nXiaoshen Han 1 ∗ Haoyu Xiong2 ∗  \nHaonan Chen 1 Chaoqi Liu 1 Antonio Torralba2 Yuke Zhu3 Yilun Du 1  \n1Harvard 2MIT 3UT Austin  \nAbstract: 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 Bspline 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: B-spline-policy.github.io](rates. More results: B-spline-policy.github.io)  \nKeywords: Fast manipulation; Visuomotor policy speedup; Action representation  \n1 Introduction  \nRobotic manipulation via visuomotor policy learning has made remarkable progress in recent years [1, 2, 3, 4] . Yet, despite these advances, task execution speed remains a major bottleneck. In everyday manipulation tasks such as folding a T-shirt, humans typically complete the task in roughly 10 seconds, whereas even stateof-the-art robotic systems often require close to a minute [5, 1, 6] . This gap exposes a fundamental and largely unresolved challenge in visuomotor policy learning—efficiency: how can robots execute complex manipulation tasks quickly, rather than merely complete them successfully?  \nFigure 1: B-spline policy. Rather than predicting a chunk of discrete-time actions, B-spline policy parameterizes actions as continuous B-spline[2]curves. This continuous representation enables temporal rescaling at execution time, allowing the policy to execute faster and more smoothly.  \nA key source of inefficiency in modern visuomotor policies lies in the design of action chunking [4, 3] . Most existing approaches parameterize actions as fixed-length chunks sampled uniformly over time. While such chunking can stabilize long-horizon prediction and improve learning performance [7], it also introduces inherent limitations:  \n• Uniform temporal resolution. Fixed-length chunks assume that every phase of a task requires identical control granularity. In practice, manipulation tasks exhibit strongly non-uniform temporal structure: fast motions such as reaching can be executed rapidly with coarse actions, whereas contact-rich phases such as grasping, alignment, and insertion demand fine-grained precision. Uniform chunking fundamentally misallocates representational capacity across time.  \n∗Equal contribution.  \n[2]A B-spline defines a smooth curve by a set of knots and control points. Instead of drawing straight lines between waypoints, a B-spline creates a smooth path that follows their overall trend. Think of it like bending a flexible ruler near waypoints instead of connecting them with sharp corners.  \n• Chunk discontinuities. Stitching together independently predicted action chunks at inference time can introduce discontinuities at chunk boundaries. While such boundary mismatches may be tolerable in low-speed or quasi-static settings, they become highly problematic during high-speed execution, where even small errors can lead to tracking failures or complete task failure.  \nThese limitations motivate a paradigm shift: representing robot actions as continuous curves rather than discrete waypoint chunks. Such a representation is used in classical motion planning [8, 9, 10, 11] and computer graphics [12, 13], where spline-based representations are widely favored for being smooth, c","cbCaiqgO5Qp0YcgH","https://ap.wps.com/l/cbCaiqgO5Qp0YcgH","pdf",1718282,1,13,"English","en",105,"# Introduction\n## Problem: Inefficiency in action chunking\n## Motivation: Continuous action curves\n## Approach: B-spline Policy (BSP)","[{\"question\":\"What is B-spline Policy (BSP) and what problem does it address?\",\"answer\":\"BSP is an action representation that accelerates robot manipulation by replacing discrete-time action chunks with continuous B-spline action curves, targeting the execution-speed bottleneck in visuomotor policy learning.\"},{\"question\":\"How does BSP represent robot actions?\",\"answer\":\"BSP parameterizes actions as continuous B-spline curves defined by knots and control points, enabling time-continuous trajectories rather than stitched fixed-length chunks.\"},{\"question\":\"How does BSP achieve faster execution without losing fidelity?\",\"answer\":\"BSP supports temporal rescaling at execution time, letting each predicted B-spline segment be retimed and sampled at higher frequencies while an inference-time segment alignment mechanism enforces smooth transitions.\"}]",1784180415,33,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"b-spline-policy-accelerating-manipulation-policies-via-b-spline-action-representations","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/b-spline-policy-accelerating-manipulation-policies-via-b-spline-action-representations/82446/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is B-spline Policy (BSP) and what problem does it address?","Question",{"text":75,"@type":76},"BSP is an action representation that accelerates robot manipulation by replacing discrete-time action chunks with continuous B-spline action curves, targeting the execution-speed bottleneck in visuomotor policy learning.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does BSP represent robot actions?",{"text":80,"@type":76},"BSP parameterizes actions as continuous B-spline curves defined by knots and control points, enabling time-continuous trajectories rather than stitched fixed-length chunks.",{"name":82,"@type":73,"acceptedAnswer":83},"How does BSP achieve faster execution without losing fidelity?",{"text":84,"@type":76},"BSP supports temporal rescaling at execution time, letting each predicted B-spline segment be retimed and sampled at higher frequencies while an inference-time segment alignment mechanism enforces smooth 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