B-Spline Trajectory Representation

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10 papers1 paper in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

SplineWAM: Adaptive Action Horizons for World Action Models via B-Spline Representations
Sep 30, 2026Jun Guo, Xiaoshen Han, Qiwei Li +7Robot Motion Generation B-Spline Trajectory Representation
Tsinghua University · Xiaomi Robotics · Shanghai Jiao Tong University +2
LieSpline-DP: Lie-Group B-Spline Diffusion Policy for Smooth Robot Manipulation
Sep 14, 2026Erxuan Xie, Bang Liu, Pingyun Nie +3Robot Motion Generation Diffusion Policy
Shanghai Jiao Tong University
SMILE: Smooth Motion for Improved Long-Horizon VLA Execution
Aug 29, 2026Jongwoo Park, E-Ro Nguyen, Kanchana Ranasinghe +3B-Spline Trajectory Representation Vision-Language-Action Models
Stony Brook University · Salesforce AI Research
ASPIRE-VINS: Adaptive Spline-based Visual-inertial Navigation System With Robust 3D Measurement Residuals
Aug 13, 2026Kwangyik Jung, Eungchang Mason Lee, Taekjun Oh +1Trajectory Optimization B-Spline Trajectory Representation
B-spline Policy: Accelerating Manipulation Policies via B-spline Action Representations
Jul 10, 2026Xiaoshen Han, Haoyu Xiong, Haonan Chen +4Robot Policy Learning Robotic Manipulation
1Harvard · 2MIT · 3UT Austin
WebSpline: Structure-Informed Splines for Real-Time 3D Gaussians from Monocular Videos
Jun 1, 2026Jongmin Park, Jeonghwan Yun, Minh-Quan Viet Bui +13D Gaussian Splatting Monocular 3D Reconstruction
KAIST
CT-VoxelMap: Efficient Continuous-Time LiDAR-Inertial Odometry with Probabilistic Adaptive Voxel Mapping
Apr 4, 2026Lei Zhao, Xingyi Li, Tianchen Deng +3B-Spline Trajectory Representation Adaptive Lidar-Inertial Odometry
ABPolicy: Asynchronous B-Spline Flow Policy for Real-Time and Smooth Robotic Manipulation
Feb 27, 2026Fan Yang, Peiguang Jing, Kaihua Qu +2B-Spline Trajectory Representation Robot Policy Learning
The authors are with the School of Electrical and Information Engineering, Tianjin University
Parallel Reference-Centric Continuous-Time Relative Localization with Augmented Clamped Non-Uniform B-Splines
Feb 25, 2026Jiadong Lu, Zhehan Li, Tao Han +4Relative Pose Estimation Multi-Robot Systems
State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, 310027, China. · Huzhou Institute of Zhejiang University, Huzhou, 313000, China. · The School of Automation and Intellegent Sensing, Shanghai Jiao Tong University, Shanghai, 200240, China. +1
BiMotion: B-spline Motion for Text-guided Dynamic 3D Character Generation
Feb 21, 2026Miaowei Wang, Qingxuan Yan, Zhi Cao +4B-Spline Trajectory Representation Text-to-Motion Generation
University of Edinburgh · Cornell University · University of Michigan +1

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SplineWAM: Adaptive Action Horizons for World Action Models via B-Spline Representations

Jun Guo, Xiaoshen Han, Qiwei Li, Nan Sun+6

Tsinghua University · Xiaomi Robotics · Shanghai Jiao Tong University+2

Robot Motion Generation B-Spline Trajectory Representation World Action Models Action Chunking Embodied World Models

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World action models (WAMs) are large embodied policies that jointly predict future video and the actions to execute, emitting a fixed-length action chunk per inference call. Such a policy allocates its computational budget uniformly in time, unable to execute for longer over free-space motion or to spend more inference on contact-rich manipulation, which limits the throughput a WAM can reach when served in the cloud. We present SplineWAM, which adaptively compresses the action trajectory into a fixed-size window of cubic B-spline parameters, fitting the knot times to the characteristics of the motion. One parameter budget then decodes into chunks of varying temporal resolution and duration, and both the executed span and the interval until the next policy call follow from the prediction itself. Aligning the video supervision to the fitted knot times of the demonstration rather than to a uniform grid concentrates the supervised frames where the action trajectory is complex. For asynchronous deployment we introduce Jacobian-Pullback Real-Time Chunking (JP-RTC), which imposes chunk continuity on the decoded raw actions the robot executes rather than on the spline parameters, and corrects the parameters through the decoder so that the executed prefix agrees with the actions already committed. On LIBERO-Plus and RoboCasa, SplineWAM improves success rate over an action chunking WAM by

8.2

and

4.4

points while cutting policy calls per episode by 22% and 26%. On three bimanual real-robot tasks under asynchronous execution, it leads or matches the baseline while decoding 1.2 to 1.6 times as much executed motion per call.