Time Optimal Execution of Action Chunk Policies Beyond Demonstration Speed
Authors: Sunwoo Kim
Jeongjun Kim
Joseph J Lim
Venue: ICLR 2026 Poster (2026)
Keywords: Accelerating Execution Speed of Imitation Policies
Time Optimal Path Parameterization
Test-time Search
Primary area: applications to robotics, autonomy, planning

Abstract: Achieving both speed and accuracy is a central challenge for real-world robot manipulation. While recent imitation learning approaches, including vision-language-action (VLA) models, have achieved remarkable precision and generalization, their execution speed is often limited by slow demonstration via teleoperation and by inference latency. In this work, we introduce a method to accelerate any imitation policy that predicts action chunks, enabling speeds that surpass those of the original demonstration. A naive approach of simply increasing the execution frequency of predicted actions leads to significant state errors and task failure, as it alters the underlying transition dynamics and encounters physical reachability constraints over shorter time horizons. These errors are further amplified by misaligned actions based on outdated robot state when using asynchronous inference to accelerate execution. Our method $\textbf{\textit{RACE}}$ address these challenges with a three-part solution: 1) using desired states as imitation targets instead of commanded actions, 2) replanning the timing of action chunks to execute them as fast as the robot's physical limits allow, and 3) employing a test-time search for an aligned action chunk that maximizes controllability from the current state. Through extensive experiments in both simulation and the real world, we show that our method achieves up to a 4x acceleration over the original policy while maintaining a high success rate

Published as a conference paper at ICLR 2026
TIME OPTIMAL EXECUTION OF ACTION CHUNK POLICIES BEYOND DEMONSTRATION SPEED
Sunwoo Kim
Jeongjun Kim KAIST
Joseph J. Lim KAIST
Seoul National University ksunw0209@smu. ac.kr
jj. kj.kim@kat st.iitt.......
joel im@kai Btockiist.a. ac. ac.kr kr
ABSTRACT
Achieving both speed and accuracy is a central challenge for real-world robot manipulation. While recent imitation learning approaches, including vision- language-action (VLA) models, have achieved remarkable precision and gener- alization, their execution speed is often limited by slow demonstration via teleop- eration and by inference latency. In this work, we introduce a method to acceler- ate any imitation policy that predicts action chunks, enabling speeds that surpass those of the original demonstration. A naive approach of simply increasing the execution frequency of predicted actions leads deviation from desired robot states and task failure, as it alters the underlying transition dynamics and encoun- ters physical reachability constraints over shorter time horizons. These errors are further amplified by misaligned actions based outdated robot state when using asynchronous inference climinate pauses during inference. Our method RACE addresses these challenges with a three-part solution: 1) using desired robot states as imitation targets instead of commanded actions, 2) re-timing action chunks to execute them fast the robot's physical limits allow, and 3) test-time search for sampling aligned action chunk that maximizes controllability from the cur- rent robot state and dynamic. Through simulation and real-world experiments, we show that our method achieves to 4x acceleration over the original policy while maintaining high success rates.
INTRODUCTION
High execution speed of robots is crucial in real-world manipulation tasks for productivity and throughput, making them practically appli- cable. Accordingly, speed an essential di- mension of improvement, par with precision and generalization. For instance, furniture as- sembly contains lot of precise insertion, and fruit packaging should generalize to different colors and shapes of various fruits, while these industrial applications require high efficiency. There are numerous other examples, such as cooking robots, surgical robots, and cleaning robots that collectively require precision, general- ization, and speed. Recent imitation learning approaches have achieved notable precision through task-specific training of action chunk policies (Zhao et al. 2023 Chi et al., 2023) and generaliza- tion via multi-task pre-training of lassionlanguage-acee
Original Policy
RACE
Figure 1: RACE. We propose method that accel-
crates the execution speed of imitation policies.
(VLA) models (Brohan et al. 2023] Black et al. (024)) on large-scale datasets (Khazatsky et al. 2024 O'illll al. 2024). However, these imitation learning methods have fundamental limitations in terms of speed: because they imitate the behavior of demonstration data, their speed is also constrained by the speed of the demonstra- tion. Furthermore, the unintuitive interface of teleoperation often bottlenecks demonstration speed, resulting in slow execution of policies, especially for nigh-precision tasks. In this work, focus
WWrk done while at KAIST CLVR lab "oorrsponniig author.

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Published as a conference paper at ICLR 2026
on accelerating the execution of imitation policies beyond demonstration, while maintaining the precision and versatility of the policies.
Intuitively, increasing action execution frequency should speed up the policy. However, acceler- ating robot imitation policies beyond demonstration speeds in this direction presents two primary challenges. First, naively increasing execution frequency results in the robot deviating from desired states for two main reasons: the underlying transition dynamics are altered because the controller has less time to execute each command, and the required high-speed movements may become phys- ically unreachable by exceeding the robot's torque and velocity limits. This deviation from the desired trajectory, which we refer to as "state error" throughout the paper, accumulates during open- loop execution when using action chunks, often leading to task failure. Second, inference delays become a speed bottleneck for increased acceleration rate. While using asynchronous inference can eliminate pauses due to inference delays, it introduces a new problem of misalignment. With asynchronous inference, the policy generates actions while previous actions are executed; thus, the new actions are based on outdated robot state, while the robot has already moved according to the previous actions. This creates a discrepancy between the plan and the robot's actual state that degrades controllability and amplifies errors.
Our main contribution is Reachability Accelerated Chunk Execution (RACE), a method that accelerates action chunk imitation policies by systematically addressing the physical challenges of moving faster than demonstrations. To counter the state error caused by altered control dynamics, RACE first train the policy to imitate the reached states from demonstrations instead of action commands, making the imitation target robust to execution timing. To follow these states as fast as possible, it then applies time-optimal path parameterization to each action chunk (i.e., "state chunk" in our algorithm), creating a dynamically feasible, adaptively timed trajectory that respects the robot's ss kinodynamic limits. Finally, to combat misalignment from asynchronous inference, RACE employs a test-time search that samples and selects the future action chunk forming the smoothest, most controllable path from the robot's current state. RACE achieves Pareto-optimal performance, reaching over 2x demonstration speed in simulation tasks and 4x original policy speed in real-world high-precision tasks, all without degrading success rates. On practical, throughput- intensive tasks, it doubles the throughput of a pre-trained Vision-Languagg-Acttinnn (VLA) model.
2 RELATED WORKS
Imitation Learning. Imitation Learning (IL) has surged with transformer- and diffusion-based poli-
cics (Zhao et al.) 2023] Lee et al. 2024 Kim et al. 2024] Chi et al., (023) Team et al. 2024
Black
let al.
2024), enabling expressive imitation of demonstrations. Action chunking (Lai et al. 2022
Zhao et al.
2023 Chi et al. 2023) stabilizes temporal behavior and shortens the effective horizon,
improving precision. Vision-Language-Action
models (Brohan al. 2023 Zitkovich et al.) 2023
Kim al.
2024 Black et al. 2024 Physical Intelligence et al. 2025) trained on large datasets (Ebert
al. 202 Walke | [023] Khazatsky et al. [2024] O'Neill al. 2024) leverage visual and lin-
guistic prior with multi-task learning, broadening generalization across manipulation tasks. While standard Imitation Learning predicts actions, our formulation of utilizing desired states relates to Learning from Observation (L.F) (Torabi et al., 2018 Burnwal et al. (025). However, unlike typ- ical LfO methods that assume fixed execution speeds, RACE uniquely adapts the execution timing of these state trajectories via optimal control to satisfy physical constraints.
Accelerating Inference Speed. Lines of work accelerate the inference speed of imitation policies. Diffusion-policy accelerations reduce sampling steps or distill to few-step policies (Heeg et al.) 2024 Prasad et al., 2024 Wang et al. 2024; Song et al. 2023b). Parallel decoding predicts action chunks in a single pass (Song et al. 2025 Kim et al. 2025). Faster inference does not by itself guarantee faster execution. We view these methods as complementary to RACE for reducing latency.
Accelerating Execution Speed. Only a small body of work directly targets the speed of execu- tion. Real-time chunking (Black et al., 2025) improves asynchronous consistency and often yields speedups. DemoSpeedup (Guo et al. 2025) accelerates policies by entropy-based downsampling
and is complementary to RACE, which can be combined at training time for additional speedup.
we use "reached state" when referring robot states in the dataset and "desired state" for states that the
policy should predict and the robot should reach during rollout
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Robet
Langunge
Time parameterized Irajeclory
000
Hlgh Conroller
Action Chunk Policy
4(5(0)
Batched Sampling
Inference delay
A Inference delay
State Trajectory
1. State as Action
II. Time Optimal Planning
III. Test-time Search
Near Tarque Conorraint
-db- Action Commond
x
Rosched
Inference delay
Near Velecity Coastraint
TEL
Dataset
13T.
Initation Target Trajectory
Predicts "desired" state action
ST.
Minimizes curvature of interpolated chunk
Figure 2: Method Overview. RACE has three main components: I. predict desired robot states as actions to make control robust under a shorter time horizon (33.1) II. time optimal planning of state trajectory that considers reachability under kinodynamic limits (3.) III. test-time search of smooth, controllable chunk given the current robot state after asynchronous inference ($3.3)
Perhaps the most similar works SAIL (Arachchige et al. 2025), which increases action frequency with task-specific rules. We compare SAIL directly in Section 4.1.3
Time Optimal Path Parameterization. Time Optimal Path Parameterization (TOPP) seeks the minimum-tinee time traversal of fixed path under kinodynamic limits. Classical Numerical-Integge Integration
(NI) methods integrate along switching curves of feasible accelerations (Bobrow et al.) 1985 Shin
& McKay, 1985, Shiller al. 1996). Convex Optimization (CO) solves for a squared-speed pro- file with improved robustness /Verccherre et al., 2009) Lipp & Boyd (014). TOPP-RA (Pham & Pham 2018) propagates controllable velocity sets with series of now-dimensional linear programs and achieves NI-like speed with CO-like robustness. RACE use TOPP-RA to retime predicted state trajectories under torque and velocity constraints.
Test-time Search and Alignment Test-time scarch/alignment (TTS/TTA) treats inference as on-the- fly optimization toward an objective-aligne distribution. Best-of-N (BoN) (Touvron et al. [023] Gui et al. 2024] Beirami et al.) 2024, [Huang et al.) 2025) is simple yet powerful approach, which samples candidates from the base model, scores them with the task objective, and returns the top one. Similarly, sampling-bast based optimization underpins classic model-based basel control, including CEM (De Boer et al. 2005) and MPPI (Williams et al. 2015), where objectives encode physical costs (Williams et al. 2018, Sundaralingam al.] (203)). 3). For learning-based robot control, value- guided MPC/CEM (Chua et 2018)) Hansen et al.) 2023) and guided diffusion (Nakamoto et al.) 2024 Wang et al. 2025) steer action inference, yet explicit alignment to physical properties remain less explored. We align action chunk sampling with trajectory-level physical criteria to improve controllability and reduce execution time under kinodynamic constraints.
RACE: REACHABILITY-AWAEL ACCELERATED CHUNK EXECUTION
Accelerating robot beyond demonstration speed pose fundamental challenge: improving the policy beyond given data, which standard imitation learning methods are not capable of. Though rein- forcement learning approaches can improve pre-trained imitation policies, they require additional rollouts, and most prior works focus on improving task success (Mark et al. 2024, Wagenmaker et al. 2025) rather than execution speed. We instead take simpler way based on imitation learning and focus on following the action sequence generated by the policy at a higher speed. We propose RACE with three main components solution:
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3.1 DESIRED STATES AS ACTIONS
When rolling out action generated by policy, we may generate and execute the action at a higher frequency, expecting the robot to reach the desired states the same rate of acceleration. We first explain why this naive approach fails. Action a leads from current state 8 to next desired state 8' following the transition dynamic P(s'ls, a). Specifically for robots, these action commands are the input to the underlying low-level controller that outputs joint torques to control the robot, where these low-level controllers modulate the transition dynamics of robot states. A common convention in current imitation learning frameworks is using the same low-level controller for teleoperation and policy execution. we execute the action at a higher frequency, that is, a shorter time for each single action, the low-level controller will lead the robot to different state, hence altering the transition dynamics. To illustrate, when using PD controller, it applies a force proportional to the difference between the current state and the action command. As the time of force applied decreases, it will lead to a different state from the original desired state, resulting in state error. Furthermore, in an open-loop execution setting, as for action chunk policies, these errors accumulate, leading to larger deviations from the desired states.
To fix this, RACE directly imitates reached states in the demonstration, training or fine-tuning the model on state and next-state pairs, instead of state and action pairs. and use it as action command for low-level controller to accurately track the desired states. Then, we can utilize different low-level controllers during rollout, perhaps with higher gain, to minimize the tracking error, i.e., discrepancy from the desired state. For instance, by using higher gain with the aforementioned PD controller, it applies a higher force to the robot, enabling the robot to reach the desired state even in shorter time. In other words, by using desired states as actions instead of original action commands together with low-level controller with better tracking during execution, Thus, this enables the robot to track the desired states, making the transition dynamics more robust to execution timing changes. Note that even when action commands are absolute actions, there is a difference between the action and the reached state, and increasing gain during teleoperation to track the action accurately will make the robot excessively reactive, making teleoperation harder. From this point, the term "action" will refer to the desired state unless explicitly stated as "action command".
Additionally, the gripper can fail grasp the target object with accelerated execution due to slow gripping. We increased the gripper speed for our method and all baselines for acceleration in our experiments for fair comparison, unless explicitly mentioned.
3.2 REACHABILITY-BASE TIME OPTIMAL PLANNING OF CHUNK EXECUTION
Even if we use the desired state directly as an action and use a different controller from teleoper- ation, the desired state may not be reachable due to physical constraints, such as joint torque or velocity constraints. With the previously illustrated high-gain PD controller, as the desired state gets farther from the current state as the acceleration rate increases, the controller will exert high force and exceed the torque constraint at some point. Thus, we need to adaptively accelerate the robot depending on the current and desired state. To solve this problem, RACE apply Time-Optimal Path Parameterization based on Reachability Analysis (TOPP-RA) (Pham & Pham 2018) on the state trajectory generated by the action chunk policy.
Given a geometric path (s)) E R representing the robot joint configuration parametrized by scalar s E [0, Send). TOPP-RA finds a time parameterization s(t) that satisfies given constraints while minimizing the total duration T. To do so, it projects generalized second-order constraints (Hauser (2014) defined by A (q)ğ + a B(q) + f(q) E into the path phase-space defined by squared velocity 5² and pseudo asceleo-acellrration
$. This yields the path constraints:
a(s)u + b(s)x + c(s) E C(s), where the coefficients are explicitly defined
(I)
Aq', b Aq" " +q" and c = f.
TOPP-RA discretizes the path into so...., sy. Given the final state set KNN, it performs a backward pass to recursively find the controllable set K,, defined as the interval of states capable of reaching K1+1 using admissible controls satisfying equation 3.2 Finally, starting from Ko. the algorithm performs forward pass that recursively selects the highest reachable x in the next controllable set.
To apply TOPP-RA in our setting, RACE interpolates the generated action chunk, which is a se- quence of state waypoints, using a cubic spline with current velocity boundary condition to de-
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fine path (s). To satisfy the boundary condition, RACE set initial state $ 1, and 8 N.in 0, &% 1 to give degree of freedom for last state. This enables the robot to follow the given action chunk in the fastest way under constraints.
However, solution of TOPP-RA doesn't always exist. When the initial state is not controllable, that is, not in Ko. it means there is no sequence of admissible controls that can follow the given path. In these cases, RACE fall back to the original control frequency without acceleration and keep replans after reaching the next waypoint until solution exists.
3.3 TEST-TIME SEARCH OF AATTROLLABILITY-AAXMMIEE ACTION CHUNK
Asynchronous inference can overcome the speedup bottleneck due to pauses from inference delays, but it introduces the unique challenge of predicting actions for future state that is inherently uncer- tain. To compensate for state execution during inference, we typically discard the first few actions of the new chunk based on the elapsed time. However, heuristic discarding fails address two fundamental IL-specific failures caused by the stochastic latency of large models. First, due to drift during the large inference window, the robot's actual state upon receiving the new chunk often differs significantly from the policy's s expected start state. This discrepancy can place the robot in a state Feurrent that uncontrollable with respect to the new trajectory, meaning the immediate torque required to merge onto the new path exceeds physical limits. Second, unlike geometric planners, generative policies are probabilistic. Small variations in state or noise can lead to bifurcation where the new action chunk is topologically inconsistent with the currently executing one. This creates sharp discontinuity at the handover point that physically impossible to track at high speeds.
Crucially, both phenomena lead to the same critical failure mode where the TOPP solver fails be- cause no valid caneeparamttrizzaioonn exists that satisfies the system's kinodynamic constraints given the misaligned initial state. While previous methods utilize action inpainting (Black et al. 2025 AAachhhhge et al. 2025) to encourage consistency, they do not explicitly guarantee physical feasi- bility under these varying constraints.
To resolve this, we utilize test-time search (TTS) to explicitly select action chunks that maximize smoothness and minimize curvature. This objective is not arbitrary and is directly linked to solv- ability. With misaligned actions or bifurcations, the path curvature at the handover point spikes and requires immense torque to change direction. By selecting the candidate that minimizes this curvature, we effectively reduce the actuation demand. This maximizes the volume of the initial controllable set Kg and ensures that the robot's current drifted state remains controllable, allowing the TOPP solver to successfully find a valid, high-speed execution plan even under severe asyn- chronous misalignment.
Specifically, we utilize Best-of-N sampling (Touvron et al. 2023 Beirami et al., 2024) where we sample multiple action chunks and select the one that maximizes following objective related to the smoothness of the path:
J (q(())
Send
(2)
So
Fend
2ds
where q(s) is given by interpolating current state and generated action sample with current velocity as boundary condition, and send in the numerator is for length normalization. q(Seas) can be interme- diate action instead of last action in the chunk, in which its index in the chunk is hyperparameter.
By optimizing this objective, we can increase the size of the controllable state set together. To see this, calculate the coefficients in Equation 3.2
a( a(s) == A(q(s)) b(s) = A(q(s)) (s) ('ss) B(q(s))a'(s),
c(s) = f((())) C(s) : C(((s))
Note that the only coefficient that contains q" is b(s), coefficient of s?. While multiple path sam- ples are generated, they are all conditioned on common initial state (q(0), (0).. Consequently, the path curvature, q" becomes the most sensitive and dominant term distinguishing the samples. Over a short planning horizon, variations in the path's 's position q are minimal, while any significant variation in the path's is tangent q' over short horizon necessitates a large magnitude in q". When the difference in q" is dominating, b(s) can vary largely, which then directly determines the size of admissible state-control pairs. Larger smoothness, i.e., smaller curvature, will lead to smaller [b(s)]
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Can
Square
Tool Hang
Door Insertion
Fruit Packaging
Trash Cleaning
Figure 3: Task Overviews. Top: Simulation Tasks, Bottom: Real-world Tasks.
and allow each state & to have larger set of admissible controls that potentially lead to the next controllable set, hence the size of the controllable set also increases. Formal proposition for this statement and proofs can be found in Appendix B
This approach shares conceptual similarities with Model Predictive Control (MPC) (Williams et al,, 2013) in using horizon- based optimization. However, unlike standard MPC, which typically uses random or gradient-based sampling, RACE utilizes the imitation policy itself as a generative sampler preserve the naturalness of human demonstrations. Furthermore, our optimization objective is novel: we optimize for controllability volume by incorporating smoothness of the path to counter the specific misaligmment challenges of asynchronous policy execution.
EXPERIMENTS
4.1 SIMULATION EXPERIMENTS
We seek to increase execution speed without sacrificing accuracy. We evaluate speed-accuracy trade-offss RACE in simulation ($4.1) and on hardware ($4.2), then analyze the contributions of -sates-as-actions ($3.I) time-optimal planning ($3.)) and test-time search ($3.3) in ablations ($4.3 $4..2 Detailed explanation of environment and task setting, baselines, and metrics are provided in Appendix
Common baselines. Action Fast-forward increases the frequency executed action commands by the same ratio (e.g. 1x, 2x, 4x) where 1x corresponds to the original policy. State Fast-forward use desired state as action and increases execution frequency by the same ratio. Action Fast-forward (Async) combines Action Fast-forward with asynchronous inference, which predicts the next chunk of action commands while execution and switches to the next chunk after discarding a number of actions executed during execution. Action Fost-forward (Inpainting) is systematically identical to Action Fast-forward (Async), but inpaints on the previous chunk, like RTC (Black et al. 2025).
For simulation experiments, which are Lift, Can, Square, and Tool Hang. While Lift and Can are sufficient with standard pick and place, Square and Tool Hang require insertion with high precision, making it more challenging for acceleration. To decompose the effect of inference delay, we use both settings with and without inference delay
manipulation tasks from Robomimic (Mandlekar et al. 202
4.1.1 WITHOUT INFERENCE DELAY
Setup. We trained diffusion policy (Chi et al. 2023) with prediction horizon Tp 32 on 200 proficient human (PH) demonstration data provided by Robomimic for 1000 epochs, save every 50 epochs, and select best performing epoch based 50 rollouts. We separately trained models that predict action commands and reached states as imitation targets. For evaluation, 200 rollouts are done for each data point. We mainly focus metrics: success rate and speedup over demon-

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Merged Performance
Merged Performance
2011
2015 20Hz
0.8
Holl
0.8
A0H2
Action Fast-forward
80
Action Fast-forward (Asyne)
State Fast-forward
Action Fast-forward (lapainting)
RACE wio TTS (Ours)
RACE (Oums)
0.6
0.6
Demonstration Speed
Demotstration Speed
Success Rate
1.0
1.2
14
1.6
1.8
2.0
2.2
1.0
1.2
1.4
1.5
18
2.0
22
2.4
26
Can
Lift
Can
Lift
1.0
1.0
1.0
1.0
0.9
0.8
1.0
1.5
2.0
1.0
1.5
2.0
2.5
1.0
1.5
2.0
25
1.0
1.5
2.0
2.5
Square
hang
Square
Tool hang
0.50
0.50
0.2
+
1.0
1.0
1.5
2.0
1.0
1.5
2.0
2.5
1.0
1.5
2.0
2.5
Speedup over Demonstration
(a) Without Inference Delay
(h) With Inference Delay
Speedup over Demonstration Success Rate plots. Left, Right: results with and without inference delay. Top: average performance over all 4 tasks, Bottom: performance for each 4 tasks. RACE (red start) achieve Pareto-optimal performance in speed-success trade-off, with up to 2x speedup without degradation in success rate, especially promi- nent in precise tasks (Square, Tool Hang). It also maintains similar performance with asynchronous inference under inference delays. Note that the standard error bars may be smaller than the marker.
Figure 4: Simulation Experiments Results.
stration, defined as the average duration of successful episodes divided by the average duration of demonstrations in the dataset. For baselines, we compare with Action Fast-forward and State Fast- forward with 20Hz (Ix), 40Hz (2x), 80Hz (4x). For RACE, we excluded test-time search since we aren't using asynchronous inference.
Results. Figure (a) (a) shows the trade-off between speed and success rate and how RACE achieve Pareto-optimal performance by improving speed while maintaining a high success rate. As accel- eration increases, the success rate of naive fast-forward methods decreases due to larger state error, especially for high-precision tasks like Square and Tool Hang. In contrast, RACE exceptionally per- forms well on these precise tasks, achieving even higher speed up compared naive fast-forward methods with 80Hz while rating a similar success rate with the original policy in Tool Hang. Even when using the desired state action, there isn' 't much improvement in the Pareto curve, confirming the importance of optimal planning by considering the reachability of the states.
4.1.2 WITH INFERENCE DELAY
The setup closely follows 4.1.1 using the same trained models. The injected inference delay 0.1 seconds, where the number of actions executed during inference depends on the acceleration rate. For basclines, used Action Fast-forward (Asyne) and Action Fast-forward (Inpainting) with 20Hz (Ix), 40Hz (2x), 80Hz (4x).
Setup.
Results. Figure 4 (b) shows how RACE perform when using asynchronous inference with inference delays. Compared to Figure (a), RACE achieves a similar success rate, indicating its robustness to inference delays. In contrast, baseline methods have larger drops in performance as the acceleration rate increases, especially in Square and Tool Hang. Even though inpainting helps improve, it's insignificant compared to RACE that achieve Pareto-optimality.
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4.1.3 COMPARISON WITH SAIL
Additionally, we directly compare RACE with SAIL (Arachchige et al. 2025). recent work that also accelerates imitation policies and outperforms prior methods such as AWE (Shi et al. 2023] and BID (Liu et al. 2025), when adapted for acceleration. SAIL similarly uses state as actions to mitigate transition dynamic change, but does not use TOPP or test-time search. Instead, SAIL. trains the model to predict whether to accelerate not based on geomet- ric complexity analysis, where the accelera- tion rate should be tuned for each task. Also, SAIL generates new actions conditioned on the previous chunk to increase action consis- tency when the state error of previous actions below some threshold. Compared SAIL, RACE adaptively selects the acceleration rate of the action chunk via TOPP at inference- time in cask-agnostie way and bypasses the need of training conditional model by uti- lizing test-time search. Comparison of the two methods is done in Robomimic setup of Arachchige al. (2025), but with torque constraints enabled. The results are shown in Table RACE outperforms SAIL in terms success rate across all tasks, hence achieving a maximal speed up without sacrificing the accuracy of the original policy. Furthermore, it also gains better speed up in precise tasks (Square, Tool Hang), demonstrating the effectiveness of RACE for precise tasks under physical constraints.
Table I: SAIL vs. RACE. Success Rate (SR) and Speedup over Demonstration (SOD) in Robomimic.
SAIL
RACE (Ours)
Task
SR
SOD
SR t
SOD
Lift
0.930
2.520
0.995
2.068
Can
0.890
1.970
0.965
1.805
Square
0.750
1.620
0.805
1.819
Tool Hang
0,610
0.940
0.715
2.053
4.2
REAL-WORLL EXPERIMENTS
4.2.1
HIGH-PRECISION TASK
Setup. For precise manipulation task in real-world,
we use Door Insertion from FurnitureBench (Heo) al.) (023). one of the most precision-demnnding subtask from the benchmark. Model training and evaluation closely follow 4.1 with some differences
0.7
10Hk
0.5
Suceess Rate
2004
including that 50 rollouts are done per each data point, with more details in Appendix E For base- lines, we used Action Fast-forward and Action Fast- forward (Async) with 10Hz (Ix), 20Hz (2x), 40Hz (4x), 80Hz (8x; not for Asyne as had near-zero success).
04
30tlx
0.3
Actin Fast forward
0.2
Action Fast Past-forward (Asyne)
* RACE (Ours)
Demonetration
Results. Similar to simulation experiment results in a.I.Tand4411. RACE achieves Pareto-optimial per- formance with maximal speedup without degrada- tion in success rate, depicted in Figure 5 One thing worth noting is that RACE not only scores similar
Speedup over Demonstration
1.0
1.2
Figure 5: Door Insertion Results.
success rate to the original policy, but achieves task completion speed far past baselines, including one with an 8x acceleration rate. We conjecture that by minimizing state error and maximizing controllability to closely follow the desired state trajectory, RACE prevent leading robots to out-of- distribution states, reducing both failures and mistakes that hinder fast task completion
4.2.2 THROUGHPUT-INTESSVV TASK
Setup. We benchmarked methods on two throughput-intensivee tasks: Fruit Packaging and Trash Cleaning. We used opensource To.s fine-tuned on DROID datase with additional fine-tuning as
"The results are directly given by the authors of Arachchige et al. 2025) to incorporate torque constraints. Note that the hase policy success rate SATI. was 66% for Tool Hang, which is lower than 69% our hase policy
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Action Fast-forward (15Hz, 45Hz)
Action Fast-forward (Asyne 15Hz, 30Hz)
(Ours)
Fruit Packaging
Trash Cleaning
Cumulative Successes
8+
20
30
60
20
30
60
Time (s)
Figure 6: Throughput-intensivy Tasks Results. Time vs. Cumulative Successes plots representing the progress of tasks over time. Both Fruit Packaging and Trash Cleaning have objects on the table. RACE achieves the highest throughput in all time.
detailed in Appendix E For baselines, we used Action Fast-forward with 15Hz (1x), 45Hz (3x) and Action Fast-forward (Asyne) with 15Hz (Ix), 30Hz (2x) since higher frequency led to severe constraint violations for Asyne.
Results. Figure 6 shows the cumulative successes across baselines and RACE the two tasks. RACE clearly reach more successes in the same time compared to baselines, establishing enhanced throughput. It evidences that RACE can speed up while maintaining the versatility of VLA. One common failure mode of acceleration baselines low throughput even with high velocity. By improving precision, RACE reduces these mistakes and fully benefits from the increased execution speed.
missing the object when grasping, resulting in
4.2.3 DYNAMIC TASK
To evaluate RACE under external dynamics, we tested a "Conveyor Belt Pick" task where the robot must pick moving juice pack (Figure Z. We fine-tuned the mo.5 model on 106 demonstrations. We evaluated performance at the original conveyor speed and a 2.5x unseen speed.
Results. As shown in Table 8 RACE maintains high success rates at 2.5x conveyor speed, whereas baselines fail. This demonstrates that RACE can generalize effectively to predictable exter- nal dynamics where the policy accounts for object motion, minimizing performance degradation.
Original Speed Time (s)
2.5 x Speed
Method
SR
SOD
SR
Time (s)
SOD
Base (15Hz)
0.03
21.3
0.61
0.00
Base (45Hz)
0.27
15.4
0.84x
0.00
RACE
0.63
9.8
1.32x
0.53
6.4
2.02x
Figure 7: Conveyor Setup
Figure 8: Real-world Semi Comi-Dynamic Task Results
4.3 ABLATIONS
4.3.1 TIME OPTIMAL PLANNING OF STATE TRAJECTORY MINIMIZES STATE ERRORS
To see how RACE reduces state error and improves performance, we compared RACE to Action Fast-forward and State Fast-forward baselines from 4.1.] We used Square, Tool Hang, and Door Insertion, the set of most precision-demandee of the current joint state from the desired joint state, defined as joont error', as a metric for state error, for both single and chunk-level actions. Figure 99 shows how minimizing state error directly contributes to improving both success rate and speed, where RACE achieves the lowest error level and highest performance. The chunk-level error plot illustrates how state error accumulates during
tasks in our evaluations. We measure the deviation

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Published as a conference paper at ICLR 2026
Acsiu Fasi-fiwward
State Fask-Garwadd
RACE (Cum)
W/b ITS
RACE (Ours)
Joint Error
Chunk-level Error (Tool Hang)
Smoothness
Controllabiltty
Throughput
1.5
0.00
1.0
10
9.06-
Valuo
0.000
0.0
Success
Speedup
Consistency
Joint Error
1,00-
0.3
0.75
-0.02
0.50
0.25
0.04
9.00
Toal Hang
Doer Insetton
Joal Homg
Dow Insrisn
-0.06
(a) TOPP Ablation.
(b) TTS Ablation.
Figure 9: (a) Joint error: deviation of current joint from desired joint, Chunk-level error: joint error for each action in the chunk. (b) Smoothness: scaled version of Eq33 Controllability: size of initial controllable set Ko, Consistency: minus of euclidean distance with previous chunk. Arrows indicate whether higher better (t) lower is better (4)
open-loop execution with naive acceleration, while RACE maintain low error. By accurately fol- lowing the desired states, RACE prevent the robot from going OOD states and lead to faster task completion.
4.3.2 TEST-TIME SEARCH ALIGNS ACTIONS WITH CURRENT STATES
To confirm that test-time search makes RACE robust to inference delay, we stress tested the al- gorithm with artificially longer inference time. Specifically, in the Fruit Packaging task, we add additional time delays to make the inference delay 0.2 seconds when the actual delay is shorter, increased timem limit to 2-minute. We compare RACE with RACE without test-time search to decompose the effect of test-time search. Metrics are defined in E.5 Figure 9b shows how test- time search improves smoothness and controllability together, supproting he claims in Section 3.3 Furthermore, increased controllability makes trajectory tracking more accurate, lowering joint error. Also, aligning actions with the current state implicitly promotes consistent actions, even without explicit objectives like inpainting. As whole, these improvements contribute higher throughput, thus performance of RACE even with high inference delays.
CONCLUSION
Practical manipulation demands both accuracy and speed, yet strong IL policies are bottlenecked by demonstration speed. We present RACE, a policy-agnostic, task-agnostic approach that (1) trains policies to predict desired state trajectories, (2) time-optimally plans the execution, and (3) selects most controllable chunks via Best-of-N during asynchronous inference. RACE achieves beyond- demonstration execution at high success in both simulation and real-world, with especially large gains on precise tasks. Together, these results alleviate the speed bottleneck while preserving the precision and generality of modern imitation policies.
REPRODUCIBILITY STATEMENT
To ensure the reproducibility our work, we provide comprehensive details on our methodology and experimental setup. Appendix E consolidates our experimental protocols, covering the poli- cies (including diffusion policies and To.s), s), datasets (RoboMimic PH and custom hardware data), evaluation metrics, and baseline configurations. Furthermore, stated in Appendix D detailed implementations our core algorithm are available in the supplementary material. We will re- lease the full codebase, complete with all configuration files, demonstration counts, and evaluation
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scripts, upon
wetpenuuuppuulippiiiipioi publication. Videos of the experrmmett available at http:://driire sOZoWZP2 OwRDgBB-xDD00B1iattt Xtt.a:/0......022222220200000000
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ACKNOWLEDGMENTS
This work was supported by Institute of Information & Communications Technology Planning & Evaluation (ITP)) grants KAIST; No.2022-0-00077, AI Technology Development for Commonsense Extraction, Reason- ing, and Inference from Heterogeneous Data; No.RS-22022-122000
Artificial Intelligence Graduate School Program,
Development of Artificial Intelligence Technology for Personalized Plug-and-Play Explanation and Verification Explana- tion), National Research Foundation of Korea (NRF) grant
Brain Pool Research Program), funded by the Korea government (MSIT), and the Technology Innova- tion Program(or Industrial Strategic Technology Development Program Robott Industry Technology Development)(RS (RS-eecm))s--2222----00-- 202--200400777 19, Dexterous and Agile Humanoid Robots for Industrial Applica- tions) funded By the Ministry of Trade Industry & Energy(MOTIE, Korea).
We thank the authors of SAIL for providing their raw data and conducting additional Tool Hang ex- periments. We also grateful Minho Heo for assisting with the FurnitueBBench setup, Junkyu Min for the DROID setup, and Doohyun Lee and Junseung Lee for initial discussions on teleoper- ation and lewelevel controllers. Finally, we thank Hyunjun Lee and Kaixin Chai for reviewing the early draft, alongside all CLVR lab members, for their thoughtful and constructive discussions.
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A PRELIMINARIES FOR PATH CONTROLLABILITY ANALYSIS
For rigor and clarity in the subsequent proofs, this section revisits and formalizes the necessary definitions from Time-Optima Path Parameterization (TOPP-RA).
Definition (Path State and Control). For given geometric path ((s), the system's 's state is defined by its squared velocity along the path, x 82. The control input the path acceleration, u
Definition (Discretized Dynamics and Constraints). The path is discretized into points so...., SN- The dynamics over segment of length A, 8++1 & given by the linear relation xi+1 3; + u,, At each point 8i, the robot's physical constraints are projected into a linear constraint on the path state and control:
a;84 + b,æ; + E C,
where C; is a convex polytope representing the set of feasible actuator efforts (e.g.. joint torques). Definition 3 (Admissible Controls and States). For given state at path point the set of admissible controls U4 (x;) is the of all path accelerations that satisfy the system's physical constraints:
Ux(x.) == (u; E R a,u; + bar, + c E.)
state I; is considered admissible this set non-empty. The set of admissible states X therefore the set of all non-negative squared velocities for which an admissible control exists:
X { E Rzo (1(),)) + 0}
Definition 4 (Controllable Set). The desired final state set, IN is given interval of squared velocities at the end of the path.
The controllable set, K.; S X, is the set of all states from which it is possible to find a sequence of admissible controls to reach the desired final state set. It computed recursively backwards, starting with KNN IN O Xx,, via the one-step set Qi:
K; = (+C+++) == {ar X Buy E (i(x;) s.t. (x; + 2A,u;) E KG++1
B PROOF OF CONTROLLABILITY MAXIMIZATION
Proposition 1. Let (s(s) and (8(8) be two piecewise C² Co-cotiinuuus paths, and let the robot's dynamic coefficient functions be Lipschitz continuous (Assumption 7 If Path B sufficiently smoother than Path A as defined by Assumptions 2 3 then for any given desired final state, the controllable state set for Path R is a superset of the controllable state set for Path A, KAA.
X11 C CCP [0,
Assumption 1 (Lipschitz Continuity of Dynamies). The functions defining the robot's dynamics, A(q), B(g), and f(g), are Lipschitz continuous with constants LA. LB, and Lj respectively over the relevant workspace.
Assumption 2 (Geometric Proximity). Path B is a small neighborhood of Path A. There exist small positive constants Eg and Eg' such that for all [0, Semal:
(s)(s) qa()))l S eg and (s's(s) (s)s)) S e
Assumption (Sufficient Smoothing Condition). Path B is significantly smoother than Path A. Let (s) = 9 (s) d (s). The reduction in the second derivative is large enough to dominate the bounded variations from the first- geometric changes. There exists margin 0 such that for any state x 2 0) and any & E [0, Send):
AA(4)) (s)x=l Ka Kalumnar|
K Kr
where Umar is the maximum possible path acceleration and Kns Kb. Ke are positive constants de- rived from the Lipschitz constants and path geometry bounds.
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Discussion of Assumptions First, we assume the system's dynamic matrices are Lipschitz contin- uous (Assumption I] This means the forces and accelerations change predictably and are bounded in response to changes in the robot's 's configuration. This assumption is standard for physical sys- tems, as the dynamic matrices are derived from continuous properties like mass and inertia, which do not exhibit discontinuities in the robot's workspace.
Second, we assume geometric proximity between the paths being compared (Assumption mean- ing candidate paths remain in small neighborhood of each other. This is direct consequence of our Best-of-N sampling algorithm. Since all action chunks are sampled from the same state and ap- plied over a short horizon, the resulting trajectories naturally form a tight bundle in the configuration space, making this assumption valid by the design of our method.
This posits that the dynamic benefit from significantly reduced path curvature (q") is large enough to outweigh the minor dynamic variations caused by the geometrie proximity. This assumption is a statement on the efficacy of our proposed objective function, (q). The objective is explicitly designed to find path that maximizes smoothness. Therefore, this assumption is that our optimization successfully finds candidate within the sampled bundle for which the "signal" of improved smoothness is stronger than the "noise" of geometric deviation.
Finally, we assume the sufficient smoothing condition holds (Assumption 3
Lemma 1. Given the Assumptions, the interval of admissible path accelerations for Path B is superset of that for Path A. (Z) 22 (xx) Vx, 2 0
Proof. The polytope C; is defined by inequalities {nIT S dg}. The path constraint thus nī (a,u; + b,2; + c;) I dg for all k. The change in the constraint function from Path A to Path is AC₃- n (Aauu + Ab,æ, + Aç)) We decompose the coefficient change Ab; into the part from q" and the part from first-order path changes: Ab; (AB,a .Y, AA, + (q's., T BB,'''. d'a,, "A.. Let Abpert be the collection of all terms in Aa, Ab, Ac; not involving q" q The change can be expressed as AC₁- n (-AA,
db.:)< + Perturbations). Using As- sumptions 1 aadd] 2, the magnitude of the perturbation terms can be bounded: I (Perturbations)] < Ka/uz] + KbT; + Kg. Assumption 3 states that the magnitude of the dominant beneficial term, [|A,,(L", " is strietly greater than this upper bound. This guarantees that AC2 nega- tive for any control u, on the boundary of admissibility for Path A, moving the constraint boundary outward. This enlarges the feasible set of accelerations, so 24,4 (x;) C U,(1))).
Proof of Proposition. The proof proceeds by backward induction on the path index i.
Base Case (i N): The controllable set at the final point is KN IN Oxxx From Lemma []] we have established that Ug (æN) C CUR( (# This implies that if state ux is admissible for Path A (i.e., ( (IN) ) is non-empty), it must also be admissible for Path B. Therefore, the set of admissible states A C x As the desired final state IN is identical, it follows that IN n X C IN n X and thus AC C K
Inductive Step: Assume that for an arbitrary step + 1, the hypothesis holds: KAA11 S K We must show this implies IC,AA C KCF. Let o; be an arbitrary state in K,,A. By definition, there exists an admissible control u,a E 21,(x:) such that the next state, x/4 = I; + uA, is in IC From Lemma | usa is also an admissible control for Path B, so U,a E (x.). Applying this control yields the same next state, z = 2441- From our inductive hypothesis. since 2441 E KC must follow that w E K We have thus shown that from state I,, there exists a control for Path B that reaches the target set KC By definition, this means 2, E K. Since this holds for any x; E KCA,, we have proven K,A1 C C.C.
By the principle of backward induction, the proposition holds for all i NO,N].
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C ILLUSTRATIVE EXAMPLES
To further motivate the components of RACE, we provide concrete examples contrasting our ap- proach with naive baselines
Desired States as Actions. Consider teleoperation scenario where user provides gentle "move forward" command. If naively executed
speed, standard controller processing action com- mands might overshoot due to momentum, By treating the reached state (geometry) as the target and using a stiff, high-gain controller, RACE tracks the intended geometry precisely rather than simply replaying motor commands that are inappropriate at high speeds.
Time Optimal Planning. Consider a pick-and-place motion transitioning from a vertical to a horizontal transfer, creating a sharp corner in the path. A naive 4x speedup would demand torque exceeding motor limits at this turn, triggering a protective stop. TOPP anticipates this bottleneck, automatically slowing down only for the high-curvature segment satisfy torque constraints, then aggressively accelerating on straight sections to minimize total duration.
Test-time Search. Due inference delay, the robot might physically drift left while the new plan assumes it is centered. Naively executing the new plan would cause a sudden "jerk" to the right. TTS samples multiple future chunks and selects that curves smoothly from the current leftward velocity, preventing control instability and bifurcation.
D IMPLEMENTATION DETAILS
Detailed implementation can be found in submitted supplementary material that contains code with the main algorithm of RACE.
D.1 SYSTEM INTEGRATION
Integrating physics-based planning into stochastic generative pipeline introduces unique system- wide challenges.
Queue Management with Timestamps. Unlike deterministic control loops, VLA inference la- tencies large and stochastic. To prevent executing outdated plans, we attach precise execution timestamps to each action chunk based on inference start time, ensuring correct temporal ordering and discard logic.
Constraint Margins. In rorldworld hardware, hitting exact torque limits can trigger protective stops due unmodeled friction noise. We apply safety margin (e.g., 90% of nominal limits) during TOPP planning to ensure robustness while still enabling high-speed execution.
E EXPERIMENT DETAILS
This appendix consolidates training and evaluation details for reproducibility. It covers common settings, baseline implementations, task-specific protocols, and metrics used throughout Section
E. COMMON ENVIRONMENT AND POLICIES
2023 for simulation and Door Insertion tasks, and To.5 (Physical Intelligence al. 2025) for Fruit Packaging and Trash Cleaning tasks.
Policies. We use diffusion policies (Chi et al.
For simulation we use RoboMimic proficient-human (PH) datasets (Mandlekar et al.) 2021) for Lift, Can, Square, and Tool Hang. For Door Insertion, collect 150 task-specific demon- strations as described below. For Fruit Packaging and Trash Cleaning, there is no additional data collected and only fine-tuned subset of DROID dataset, detailed below.
Datasets.
Evaluation metrics. We report Success Rate (SR) and Speedup over Demonstration (SOD). SOD is defined as the ratio between the average duration of successful episodes and the average duration of the demonstrations the dataset. When plotting SR vs. SOD, each point corresponds to one frequency setting method variant.
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E.2 BASELINE IMPLEMENTATIONS
Action Fast-forward. This baseline accelerates the base policy by executing its action commands at higher frequency without any timing or dynamics compensation. In simulation, we evaluate 20 Hz (original), 40 Hz (2x), and 80 Hz (4x), using the same action horizon 16 across methods. The policy produces a chunk of Ta actions; as soon as the previous chunk finishes executing, the next chunk is generated and executed back-to-bar (i.c., no inference-delay handling in this synchronous setting).
State Fast-forward. This variant feeds the policy's desired states (learned "reached states") di- rectly as the command stream at 40 Hz and 80 Hz. mirroring Action Fast-forward's 's acceleration but with state targets instead of action commands. It is equivalent adopting only the first compo- nent of RACE (using desired state as action) without time-optimal path parameterization (TOPP) or test-time search, and it uses T. 16 like the other simulation baselines.
Action Fastforward (Async). This is the asynchronous acceleration baseline. While the robot executes the tail of the current chunk, the policy simultaneously infers the next chunk; upon comple- tion, we discard the portion of the current chunk already executed during inference and then switch to the freshly generated chunk. In simulation, inference is triggered immediately after the previous inference finishes (purely asynchronous scheduling). In the real world (Door Insertion), we adapt the trigger: the policy begins inference when the remaining actions in the current chunk fall below Naiscard plus a safety margin ("spare" actions), using 7.=24 and 8 spare actions to cushion unex- pected latency. Naiscard is estimated from a queue of observed inference delays using the maximum latency as a robust bound; we then set Ndiseard = [eeeee Fint] once the next chunk is ready. We evalu- ate at 10/20/40/80 Hz; note that 80 Hz is excluded for Action Fastforward (Async) in Door Insertion due to near-zero success at that rate.
Action Fastforward (Inpainting). This baseline augments Action Fastforward (Async) by condi- tioning the newly generated chunk on the previously executed chunk (inpainting). following prior asynchronous control practices such as RTC (Black et al. 2025) and SAIL (Arachchige al. (025). Concretely, we employ pseudoinverse guidance (Song et al. [023a] with diffusion models to guide the next-chunk generation toward consistency with the trailing segment of the prior chunk; an anal- ogous adaptation for flow models (Pokle et al. 2023) is used in RTC. Operationally, scheduling and chunk swinc-switching mirror Action Fastforward (Async); the difference is the conditional sampling that encourages cross-chunk continuity.
Setting-specific hyperparameters. Simulation (Robomimic). All synchronous baselines use T,=16; acceleration rates are 20/40/80 Hz. Action Fastforward (Async) and Action Fastforward (In- painting) use the same action-frequency grid and purely asynchronous scheduling (no extra safety margin). Real-world: Door Insertion. Action Fast-forward pauses after executing chunk of T.=16 while waiting for the next chunk; Action Fastforward (Async) uses T, 224 with 8 spare ac- tions and max- matelatency queue for robust discard estimation; we test 10/20/40/80 Hz and omit 80 Hz for the async baseline due to failures. Real-world: Throughput tasks. For Fruit packaging and Trash Cleaning, Action Fast-forward uses T,=8 and is evaluated at 15/45 Hz; Action Fastforward (Async) uses T.=15 with 7 spare actions at 15/30 Hz. to avoid severe constraint violations observed at higher rates.
Notes intent and limitations. These baselines isolate distinct failure modes under acceleration: (i) Action Fast-forward stresses state-tracking under faster open-loop actions; (ii) State Fast-forward tests whether using desired state targets alone (without TOPP) suffices; (iii) Action Fastforward (Async) exposes misalignment from inference latency; and (iv) Action Fastforward (Impainting) ex- amines whether chunk-consistency alone mitigates asyne misalignment. Together, they form the reference set used in the main text comparisons and ablations.
E.3 SIMULATION PROTOCOL.
Tasks. Lift, Can, Square, Tool Hang from RoboMimic (Mandlekar et al, 2021). Square and Tool Hang require precise insertion and sensitive tracking error when accelerating.
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Training. We train diffusion policies with prediction horizon T,=32 on 200 PH demonstrations for 1000 epochs, checkpoint every 50 epochs, and select the best epoch via 50 rollouts. We train separate models that predict action commands and desired (reached) states imitation targets.
Evaluation. Unless stated otherwise, we average 200 rollouts per point.
Frequencies. We evaluate 20, 40 (2x), and 80 Hz (4x). Action horizon 16 unless specified.
Synchronous setting. A new chunk is generated only after the previous chunk finishes execution, which isolates tracking/controllabiitty effects without inference delay.
Asynchronous setting. We inject a 0.1 inference delay. During inference the running chunk continues; when the next chunk arrives we discard and replace the actions produced during the delay window. Exact discard rules are shared across methods.
E.4 REAL-WORLD PROTOCOL
High-precision: Door Insertion (FurnitureBench). We use the FurnitureBench cabinet setup (Heo et al. 2023) and evaluate the Door Insertion subtask that requires precise insertion after place-
ment.
Training. We collect 150 demonstrations with SpaceMouse and train diffusion model that jointly predicts action commands and desired states; we use the checkpoint at epoch 800 for all methods.
Baselines.
Action Fast-forward: same as simulation, but in hardware the policy pauses after executing a chunk of length T,=16 to wait for the next chunk.
Action Fast-forward (Async): inference starts when the remaining actions in the current chunk fall below the expected number of discarded actions plus 8 spare actions; we set =4=24. The time between inferences adapts to the acceleration rate. We estimate the number of discarded actions using the maximum over running queue of recent delays, similar to Black et al. 2025
Frequencies. 10, 20 (2x), 40 (4x), and 80 Hz (8x). We exclude 8x for Action Fast-forward (Asyne) due near-zero SR.
Evaluation, 50 rollouts per point.
Fruit Packaging and Trash Cleaning. We used opensource m0.5 fine-tuned on DROID datase fl with additional fine-tuning on subset of DROID (task put, place, pick, move, object : marker, cup- cup-object, block) for two seperate model that predicts action command and desired state. Each task uses language instruction ("Pиt the fruits in the box., "Put the trashes in the trash bin."). We evaluate cumulative successes over 60 with 15 objects and rollouts per method. One fruit placed in the box one trash placed in the bin counts as success.
Throughput:
Models. We start from the open-source Tu.s checkpoin and apply additional fine-tuning on DROID subsets. We train two variants that predict action commands desired states, respectively, and use them consistently across methods.
Baselines and frequencies
Action Fast-forward: T.=8 at 15 and 45 Hz (3x).
Action Fast-forward (Asyne): T.=15 with 7 spare actions at 15 and Hz (2x). Higher rates caused severe constraint violations in our setup.
E.5 METRICS
Success Rate (SR). Fraction of rollouts that satisfy the task's success predicate.
Speedup over Demonstration (SOD). SOD
ave. duration of successful episodes The demonstration dura- avg. duratioe demonstrations
tion measured from the training set statisties for the corresponding task.
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Joint error. Time-averaged &g distance between executed joint positions and the retimed desired joint trajectory.
Smoothness. Equation 3.3 which is ure-length-normaliiee curvature of the joint-space spline fitted to a chunk, multiplied T00 for scaling. Lower curvature implies higher tolerance to torque and velocity limits, hence larger controllable sets.
Controllability. Size of the initial controllable set Kop for the retimed trajectory under joint velocity, acceleration, jerk, and torque limits.
Consistency. Negative Euclidean distance between the new chunk dg.v and the exccuted segment of the previous chunk d + at the handoff: ||00:v a sgs+vlll where N is the number of actions executed during inference and 8 is the starting index in the previous chunk.
E.6 COMPARISON TO SAIL
We reproduce the SAIL. evaluation protocol (Arachchige al. 2025) with the following adjustments for comparability:
Enable torque constraints to test reachability under realistic physical limits.
Use fixed delay of action steps rather than wall-clock time to align asynchronous scheduling.
Sweep identical frequency grids and use the same controller as in our experiments.
Table reports SR and SOD under these settings.
F ADDITIONAL. EXPERIMENT RESULTS
COMPONENT-WIR ABLATION
To disentangle the contributions of TOPP and TTS, evaluated RACE variants the precision- demanding Square and Tool Hang tasks. As shown in Table removing TOPP significantly reduces speed (SOD), while removing TTS degrades Success Rate (SR). RACE achieves Pareto-optimal performance only when both components are combined.
Table 2: Component-wise Ablation on Simulation Tasks
Square
Tool Hang
Method
SR t
SOD
SR 1
SOD
RACE (Ours)
0.86
1.74
0.62
1.86
w/o TTS
0.81
1.74
0.59
1.85
w/o TOPP
0.85
1.42
0.61
1.36
w/o TOPP, TTS
0.82
1.43
0.59
1.39
G VIDEOS
Videos of real-world experiments be found in httpappp///deeee..opp..c/oo/tttp 27follaess111dee22ooc2e22oooooo0
H
LLM USAGE
LLMs are used for polishing writing in Section 2 5 and Appendix
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Reviews:
[{"review_id": "xunwuyD7dW", "reviewer": "Reviewer_zmC2", "summary": "RACE improves the test-time execution speed and control frequencies of action chunk policies. Given a predicted path, RACE uses Time-Optimal Path Parameterization to plan an alternative time-optimal trajectory within given physical constraints. To compensate for the state discrepancies caused by async inference, RACE samples multiple action chunks and selects the one that aligns the best with the current state based on trajectory smoothness, which also benefits time-optimal trajectory replanning. RACE is evaluated in multiple simulation and real-world domains and sees improvement in execution speed with limited degradation in success rate.", "questions": "- Is it possible to establish a Pareto front by, for example, changing the constraints in TOPP?\n\nTypos / Minor issues:\n- Missing subfigure captions in Figure 7.\n- L46, the citation is broken.\n- L243, $a$, $b$, $c$ should be bold.\n- L247, missing closing parenthesis.\n- L361, \"chang,e\".", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The presentation of the paper is overall good, although some parts lack clarity.\n- The proposed method is novel to my knowledge.\n- RACE is empirically evaluated to improve over baselines in both simulation and real-world benchmarks, including tasks with high precision requirements that can be challenging to speed up.", "weaknesses": "- A baseline where you directly apply test-time search to action/state fast-forward to smooth the trajectories is missing. This baseline can better contextualize the contributions of both TTS and TOPP. Same for RACE w/o TTS. Figure 7b shows the improvement of TTS in some indirect metrics, but you can also add the evaluation result of RACE w/o TTS to, for example, Table 1.\n- Paragraph starting from L183 could use more context and notation explanations.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "jCWnab6tSU", "reviewer": "Reviewer_gaD1", "summary": "The authors propose an approach to speed up policy inference at inference time. Their proposed method, RACE, uses states as imitation actions, optimizes for the timing of state chunks, and employs a sampling-based replanning mechanism for asynchronous execution. Experiments are conducted in both sim and real, testing the task success rate v.s. speedup rate. Ablations are done to showcase lower state deviation and higher smoothness of the method.\n\nOverall, the motivation of the paper is clear, the method is sound, and the experiments results are strong. However, I have a few questions/concerns that could be addressed by the authors to further improve the paper. I'm happy to raise my score if the concerns are properly addressed.", "questions": "* How does SAFE scale with the state space of the robot? if the robot is a humanoid, what are some of the considerations to adapt SAFE to it?\n* What is the effect of increasing the size of controllable state set? will this result in better time parameterizations?\n* What is the point of testing the methods with no inference delay?\n* Is there any point of using test-time search when there is no inference delay? \n* Does SAFE work in non-quasistatic environments. For instance, playing air-hockey.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* Speeding up policy execution while maintaining performance is an exiciting area of research and has real world impacts\n* The paper is written well and easy to follow (besides some minor typos)\n* Main experiment results are good, the choice of baseline and tasks are appropriate\n* Real world results further strethen the authors' claim about the method", "weaknesses": "* The test-time search part of the algorithm seems to be disconnected from the rest, and the objective to optimize for smoothness seems a little arbitrary. Is this component added just to overcome some issues of asynchronous inference, or does it have some ties with the time-optimal planning (Section 3.2)?\n\n* The authors should also try to explain whether the test-time search component can prevent issues from (Black et al. 2025, Figure 2), where bifurcation between consecutive chunks can exist.\n\n* Following the previous point, there seem to be ablations missing: what is the performance when you remove time-optimal planning and only include test-time search, and vice versa?\n\n* The method section describes multiple design choices and their reasoning (using state vs. actions, using a high-gain PD controller vs. time-optimal planning, naive asynchronous execution vs. test-time search), but they can be improved with examples. It will be beneficial to include visual explanations in addition to the text.\n\n* There are apparent typos, incomplete captions, and citation errors throughout the paper. Here are some examples. The authors should fix these and spend effort double-checking the paper for errors.\n\n  * line 46 citation missing\n\n  * line 47–48 “it imitate” → “it imitates”, “it’s speed” → “its speed”\n\n  * line 85 citation missing\n\n  * line 178 “the controller will high force”\n\n  * line 242 Equation 3.2 is missing\n\n  * Captions for Figure 7(a) and 7(b) are placeholders", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "GhJRsftUsM", "reviewer": "Reviewer_XTQT", "summary": "I believe that the three main contributions are primarily established control engineering techniques. The slow execution rate of demonstration data necessary for learning reflects a fundamental limitation of the imitation learning paradigm, which is ideally not addressed by downstream control engineering. If we consider the domains in which these techniques are applicable, it is necessarily quasi-static problems where the dynamics of the task are wholly controllable by the agent. This would exclude eventual dynamic tasks like playing ping pong, throwing and catching, or anything else involving external dynamics not rigidly attached to the robot. In this sense, there is relatively shallow vision aside from the integration of these techniques into VLA systems.\n\nHowever, control engineering has always yielded reliable sub-systems for autonomous systems and, naturally, current industry engineers relying on VLA systems stand to benefit from learning about this work. Beyond this, it is challenging for me to envision how other researchers may fundamentally build upon what is presented.", "questions": "Given the extensive optimal control prior (model-based trajectory optimization), are iLQR or MPPI valid baselines? If so, why not compare them? I surmise by “explicit alignment to physical properties remains less explored”, the authors mean that physical constraints are not often included in MPC and CEM. This is certainly not true considering that MPC has traditionally been performed with models and actuation constraints and is actually often the baseline for model-based control. Here is an open-source project for manipulation with joint-level control under constraints: (Bhardwaj et al.). \n\n\n### References\n\nBansal, S., Chen, M., Herbert, S., & Tomlin, C. J. (2017). Hamilton-Jacobi reachability: A brief overview and recent advances. 2017 IEEE 56th Annual Conference on Decision and Control (CDC), 2242–2253. https://doi.org/10.1109/CDC.2017.8263977\n\nBhardwaj, M., Sundaralingam, B., Mousavian, A., Ratliff, N. D., Fox, D., Ramos, F., & Boots, B. (2022). STORM: An Integrated Framework for Fast Joint-Space Model-Predictive Control for Reactive Manipulation. Proceedings of the 5th Conference on Robot Learning, 750–759. https://proceedings.mlr.press/v164/bhardwaj22a.html", "limitations": "", "rating": 2, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 1, "strengths": "1. Demonstrable speed up on door insertion in the real-world\n2. Experimentation and result plots are clear and easy to interpret quickly\n3. Clear motivation and takeaway", "weaknesses": "On contributions\n1. “Imitation from states instead of actions” is effectively learning-from-observation but this area’s related work is not discussed at all. For instance, learning an inverse dynamics model might be a valid and more generalizable alternative to this works’ model-based trajectory optimization.\n\n    * In the first place, if the “action” is not the robot actuation (e.g. motor commands) but some function of state, the learning problem did not originally have actions to begin with. This is an important distinction because almost all robot actions are underactuated relative to state. Hence, this is why modern VLAs rely heavily on controllers to do position control, but the modern language and this work seems to have equivocated delta state as actions. In other words, the first claimed contribution of “actions as state” implicitly existed already in standard VLAs.\n\n    * In the way it is presented, I must assume that the model used for the Time-optimal planning pertains only to the robot (it would be a significantly different and substantial contribution to apply time-optimal planning for exteroceptively sensed states, like a manipulated hammer or ball). This also implies that following controllability and reachability analyses are done only on robot state. However, under this scope, these analyses effectively reduce to optimal control for trajectory tracking. Reachability is often otherwise used in safety analyses for domains such as autonomous driving or social navigation where unsafe and dynamic constraints can be highly non-convex and correlated across uncontrollable states (Bansal et al.).\n\n2. Test-time search and controllability. As noted in the related works, this is very much equivalent to MPC.\n\n3. The work acknowledges that many of the individual techniques exist with a substantially rich history in the related works. I appreciate that the authors do not overclaim the contributions. However, it is perhaps misleading to dedicate such large portions of the manuscript (sections 3.1-3.3) to existing techniques. The authors may save on space by citing references and discussing integration or system-wide challenges rather than the individual methods. For instance, section 3.2 uses state as the time-optimal model parameterization, but states in the context of VLAs and their pertinent tasks are much more than just the robot state.\n\n4. Parts of the paper are not well-proofread and remain unpolished:\n   * Caption of figure 7", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "xolGYGwRUq", "reviewer": "Reviewer_qRQC", "summary": "The paper proposes RACE, a method that accelerates robot policies beyond expert demonstration speed. RACE uses TOPP-RA to refine the state trajectories proposed by the learned policy, generating new trajectories that satisfy physical constraints. When inference delays occur as in real-world settings, it applies Best-of-N sampling over action chunks to maximize trajectory smoothness. Experiments on Lift, Can, Square, and Tool Hang tasks show that RACE outperforms baselines in both simulation and real-world environments.", "questions": "1. Does RACE's performance drop down when the backbone policy does not output the entire trajectory?\n2. Several typos in the paper need correction: citation errors in line 46, 82, 85, \"chang,e\" in line 361,  missing a space in line 320.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The paper conducts extensive experiments across four manipulation tasks in both simulation and real-world environments. To evaluate RACE’s robustness and trajectory quality at test-time, the paper designs rich metrics including action smoothness, joint error, and consistency.\n2. Real-world demos are provided, showing that RACE performs well at different control rates (15Hz, 30Hz, 45Hz).\n3. RACE can accelerate various backbone policies learned by imitation with no additional training in the backbone policy, like diffusion policy and VLA.", "weaknesses": "1. RACE requires the backbone policy to output the entire state trajectory. It can't accelerate policies that only produce single actions or action sequences.\n2. When using TOPP-RA, RACE needs accurate knowledge of the robot’s physics constraints, such as maximum torque and velocity.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)