Robot manipulation data collection has been shifting from teleoperation toward robot-free demonstrations, through interfaces such as the Universal Manipulation Interface (UMI) or directly from human hands. Vision-Language-Action (VLA) policies trained on such data inherit the demonstrator's timing. Yet human timing does not directly transfer to robots: compliant hands tolerate fast contact, whereas robots may overshoot due to actuator and tracking limitations; conversely, robots can move faster in free space. This motivates a unified approach that reconciles execution speed with contact safety. We present RoboPace, an online retiming layer that preserves the policy's geometric path while adapting its timing, respecting the target robot's kinematic and dynamic constraints. It adapts execution speed based on predicted contact, jointly accounting for contact-dependent speed limits and the robot's motion constraints. The method requires no policy retraining and operates in real time. Across three contact-rich tasks on a dual-arm robot, faster uniform execution and physical-limit-only retiming largely fail. RoboPace instead achieves higher overall success than slow uniform execution while completing four of five commands in approximately half the time, retaining the reliability of slow execution without its time cost.
Figures & tables
Figure 1: Contact-Aware Retiming of Action Chunks: RoboPace preserves the geometric path a policy predicts and adapts only its timing while respecting the robot’s kinematic and dynamic constraints: predicted contact imposes a speed limit on the hands at contact-critical waypoints (red), while free-space motion (green) runs as fast as those constraints allow. On a dual-arm robot, this achieves higher overall success than uniformly slow execution while completing four of five commands in about half its time.
Figure 2: RoboPace System Overview: TOPP-RA retimes each VLA action chunk as a joint path, limiting speed at waypoints where contact is predicted. This accelerates free-space motion while slowing at contact.
Figure 3: RoboPace Pipeline: Geometric and camera cues identify contact-critical waypoints for TOPP-RA retiming. The play-out feeds its commanded velocity into the next solve, allowing consecutive action chunks to join without stopping.
Figure 4: Contact Predictor Evaluation: (a) predicted and ground-truth stc on one Cup test clip, (b) mean absolute error (MAE) versus ground-truth stc , and (c) per-task MAE at contact onset ( stcgt=0 ). At contact onset, MAE is below 1.3 source steps for all tasks.
Figure 5: Comparison with Baselines: Accelerated execution and physical-limit-only retiming rarely succeed, whereas decelerated execution is reliable but slow. RoboPace achieves 32 successes in 50 trials and completes four of five commands in about half the decelerated execution time.
Figure 6: Execution Trace of a Bimanual Handover: One run of Part , showing that RoboPace slows as the right hand approaches the part and during hand–hand contact, while accelerating elsewhere. Red and green indicate gated and ungated motion, respectively.
vcontact (m/s)
schedule
0.01
0.02
0.04
Linear- N3
0.6 (23.1)
0.8 (16.1)
0.4 (10.0)
Linear- N9
1.0 (26.3)
0.2 (13.1)
0.0 (–)
Cosine- N3
0.8 (25.4)
0.4 (15.6)
0.2 (8.0)
Cosine- N9
0.6 (23.8)
0.8 (22.4)
0.2 (8.0)
Tanh- N3
1.0 (32.5)
0.8 (16.9)
0.2 (9.0)
Table 1: Sensitivity to the contact speed limit and transition schedule. (a) Five trials per setting on Pyramid phase 2: success is more sensitive to the contact speed limit, with lower limits trading speed for reliability. (b) Ten trials per command across five commands: Linear- N3 (bold) achieves the highest overall success ( 32/50 ) among the three lowest-cost schedules selected at 0.02 m/s and is used as the default.