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arxiv: 2604.02523 · v1 · submitted 2026-04-02 · 💻 cs.RO

Recognition: 2 theorem links

· Lean Theorem

Tune to Learn: How Controller Gains Shape Robot Policy Learning

Antonia Bronars , Younghyo Park , Pulkit Agrawal
Authors on Pith no claims yet

Pith reviewed 2026-05-13 20:59 UTC · model grok-4.3

classification 💻 cs.RO
keywords robot learningposition controlbehavior cloningreinforcement learningsim-to-real transfercontroller gainspolicy learningmanipulation
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The pith

Controller gains for robot policy learning should be chosen according to the learning method rather than the target task stiffness.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines how the numerical gains on position controllers change the outcomes of three common robot learning pipelines. Experiments across tasks and robot bodies show that behavior cloning succeeds more readily with compliant overdamped settings, reinforcement learning reaches success in every gain regime once hyperparameters are matched to it, and sim-to-real transfer degrades under stiff overdamped gains. The key shift is that stiffness no longer comes from the controller alone; it emerges from the learned policy reacting through the controller. A reader cares because position controllers are now the standard way to run learned policies, so choosing gains by the wrong criterion wastes effort or prevents learning altogether.

Core claim

Systematic tests demonstrate that position controller gains affect learnability differently across paradigms: behavior cloning benefits from compliant and overdamped regimes, reinforcement learning succeeds across all regimes when hyperparameters are tuned compatibly, and sim-to-real transfer is harmed by stiff and overdamped regimes. Effective stiffness therefore arises from the interplay between the learned reactions and the control dynamics rather than from the gains in isolation.

What carries the argument

Position controller gains viewed as a learnability filter that modulates the interaction between the policy output and the robot's closed-loop dynamics in behavior cloning, reinforcement learning, and sim-to-real pipelines.

If this is right

  • Behavior cloning performs reliably only when the controller is set to compliant and overdamped gains.
  • Reinforcement learning from scratch can succeed in any gain regime once its hyperparameters are adjusted to that regime.
  • Sim-to-real transfer success drops when stiff and overdamped gain settings are used.
  • Gain selection must be decided by the learning paradigm in use rather than by the compliance desired at execution time.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Practitioners should decide controller gains at the start of a project once they have chosen imitation learning versus reinforcement learning.
  • The same gain-paradigm dependence may appear when other low-level controllers such as velocity or torque interfaces are substituted for position control.
  • Policy architectures that explicitly model the controller dynamics could reduce or eliminate the need for separate gain tuning.

Load-bearing premise

The tested tasks, robots, and hyperparameter regimes are representative enough that the observed patterns will hold for other manipulation settings and learning algorithms.

What would settle it

Repeating the exact experimental protocol on a different robot embodiment or task and finding that behavior cloning performs best under stiff gains instead of compliant ones would falsify the central claim.

Figures

Figures reproduced from arXiv: 2604.02523 by Antonia Bronars, Pulkit Agrawal, Younghyo Park.

Figure 1
Figure 1. Figure 1: Different robot learning paradigms prefer different controller gain interfaces. Colored regions indicate gain regimes where each paradigm succeeds. Contrary to conven￾tional wisdom of tuning gains for desired task compliance, optimal gains depend on the learning paradigm. Based on our experimental findings, heatmaps illustrate representative gain preferences for (a) behavior cloning, which favors compliant… view at source ↗
Figure 2
Figure 2. Figure 2: Controller gains induce diverse action–response dynamics. We evaluate a broad range of representative gain configurations and their resulting dynamic responses to assess their impact on learnability. Once we recognize controller gains as learning interface parameters rather than behavioral parameters, the design ques￾tion becomes: which interface properties facilitate learning? And critically, do different… view at source ↗
Figure 3
Figure 3. Figure 3: Tracking response curves from existing robot datasets [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Task-level impedance can be decoupled from low￾level controller gains with learned policies. A learned policy can achieve (a) compliant behavior despite stiff low-level gains, and (b) stiff behavior despite compliant gains. characteristic of stiff controllers. This pattern was prevalent across datasets, suggesting stiff gains have become an implicit default in data collection. III. DECOUPLING GAINS FROM TA… view at source ↗
Figure 7
Figure 7. Figure 7: Box Pushing Task. fixed goal pose. We chose this task because it requires both precision and sustained contact, yet remains achiev￾able even under unintuitive gain configurations. A critical consideration is that the mapping from user input to commanded position target, ϕ(u, x) → xdes ( [PITH_FULL_IMAGE:figures/full_fig_p004_7.png] view at source ↗
Figure 5
Figure 5. Figure 5: Behavior cloning prefers compliant and overdamped controller gains. Closed-loop rollout success rates across a grid of proportional (Kp) and derivative (Kd) gains for diverse manipulation tasks and robot embodiments. Each heatmap reports success averaged over evaluation rollouts. Across tasks, higher success rate (darker red) consistently concentrates in the compliant, overdamped regime (upper-left), while… view at source ↗
Figure 6
Figure 6. Figure 6: Any teleoperation system requires a mapping [PITH_FULL_IMAGE:figures/full_fig_p005_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: Compliant controllers attenuate action errors. (a) Vali￾dation MSE loss during training: compliant gains yield higher loss, while stiff gains achieve lower loss. (b) Open-loop success rate under action noise: compliant gains maintain high success while stiff gains completely fail. (c) Compliant gains keep the perturbed trajectory close to the original, while (d) stiff gains cause large deviations that lead… view at source ↗
Figure 9
Figure 9. Figure 9: Teleoperation performance under different gain regimes. With optimized input mapping ϕ ⋆ (K) (Eq. 7), compliant and over￾damped controllers (grid top-left) achieve similar or better success rates, user ratings, and shorter completion time to stiffer settings. MSE), but the controller attenuates the resulting errors during execution, yielding better closed-loop performance. Result V-A-II (Effect on Teleoper… view at source ↗
Figure 11
Figure 11. Figure 11: End-to-end BC pipeline still favors compliant and over￾damped gain regime. When data collection and policy training are performed end-to-end under each gain setting (see Section IV-A), the compliant, overdamped regime achieves the highest success rate ( [PITH_FULL_IMAGE:figures/full_fig_p007_11.png] view at source ↗
Figure 10
Figure 10. Figure 10: RL training across gain regimes. (a–b) Success rate across the hyperparameter landscape varies among gain settings and tasks; policies with 95%+ success rate (green circles) are found across all conditions. (c–d) Sample efficiency and training stability of PPO is comparable across gain regimes for both tasks. B. Reinforcement Learning Result V-B-I (RL Solution Existence): Reinforcement learning can discov… view at source ↗
Figure 12
Figure 12. Figure 12: Stiff and overdamped gain settings yield lower SysID modeling errors, but exhibit larger closed-loop Sim2Real errors. Policy observations during closed-loop rollout evolve similarly be￾tween sim and real (b-left) for compliant, overdamped gains, but very dissimilarly (b-right) for stiff, overdamped gains. Result V-B-III (RL Sample Efficiency): Sample effi￾ciency and training stability are comparable acros… view at source ↗
Figure 13
Figure 13. Figure 13: Stiff and overdamped gain settings reduce sim2real transferability. The Sim2Real trajectory error (Eq.11) is consistently larger (light blue) in the stiff and overdamped regime (a-c). The primary Sim2Real failure mode is high-frequency oscillation (d). Result V-C-III (Effect of Policy Frequency): Lowering the policy frequency (increasing the zero-order-hold du￾ration ∆t per policy action) reduces the prev… view at source ↗
Figure 14
Figure 14. Figure 14: Jitter Failures vs. ∆t. We detect jitter failures by computing the maximum per￾joint standard deviation of joint velocity during the fi￾nal 2 seconds of each rollout, flagging trajectories exceed￾ing a threshold of 0.04 rad/s; this metric reliably separates the two modes, as settled roll￾outs have a median velocity standard deviation of 0.001 rad/s while jittering rollouts have a median of 0.675 rad/s. As… view at source ↗
Figure 15
Figure 15. Figure 15: Effective Cartesian stiffness throughout training for the two counterintuitive pairings. Despite 32× lower actuator stiffness, the stiff-behavior policy achieves ∼5× higher effective task-level stiffness than the compliant-behavior policy [PITH_FULL_IMAGE:figures/full_fig_p013_15.png] view at source ↗
Figure 18
Figure 18. Figure 18: Non-prehensile box manipulation task for the user study. A single trial of the task involves teleoperating the robot from a reset pose to make contact with the box, then pushing the box towards the goal. The task is complete when the green square is completely occluded by the box (b). As shown in Table II, H0 is rejected for every task with p ≪ αadj, providing strong evidence that the compliant-overdamped… view at source ↗
Figure 20
Figure 20. Figure 20: Five tasks for online RL solution existence proof. For each task, we trained a successful policy for 8+ gain configurations spanning the range of stiff / compliant, overdamped / underdamped. TABLE III: Action representation across RL tasks. Task qref(t) G1 G2 Gripper FR3 Joint-Reach q(t) q0–3 (elbow) q4–6 (wrist) – FR3 EE-Reach q(t) q0–3 (elbow) q4–6 (wrist) – FR3 Lift Cube q(t) q0–3 (elbow) q4–6 (wrist) … view at source ↗
Figure 21
Figure 21. Figure 21: Action representation. The policy output is scaled by a per-joint-group vector α and added to a reference position qref to produce the position target qdes sent to the PD controller. 2) Action Representations: For all tasks, the position target sent to the low-level PD controller at each timestep is: qdes(t) = α ⊙ πθ(st) + qref(t) (26) α = [α1, . . . , α1 | {z } G1 , α2, . . . , α2 | {z } G2 ] where qref(… view at source ↗
Figure 22
Figure 22. Figure 22: System identification result for sample gain settings in each gain regime. We show commanded positions (green), real-world achieved positions (orange), and simulation positions (blue) achieved with the optimal actuator parameters [PITH_FULL_IMAGE:figures/full_fig_p018_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: Behavior cloning performance across dataset size. Success rate per gain setting for the Block Stack task. The preference for compliant and overdamped gain settings is maintained across dataset sizes (a-c). 16 32 64 128 256 512 1024 2048 Proportional Gain (Kp) 1 2 4 8 16 32 64 128 Derivative Gain (Kd) 0.04 0.24 0.14 0.18 0.41 0.36 0.29 0.28 0.43 0.33 0.40 0.31 0.56 0.31 0.48 0.19 0.93 0.79 0.66 0.65 0.50 0… view at source ↗
Figure 24
Figure 24. Figure 24: Behavior cloning performance across policy architectures. Success rate per gain setting for the Block Stack task. The preference for compliant and overdamped gain settings is maintained across policy architectures (a-c). 16 32 64 128 256 512 1024 2048 Proportional Gain (Kp) 1 2 4 8 16 32 64 128 Derivative Gain (Kd) 0.36 0.11 0.08 0.30 0.69 0.31 0.52 0.45 0.44 0.22 0.25 0.53 0.39 0.41 0.49 0.34 0.62 0.80 0… view at source ↗
Figure 25
Figure 25. Figure 25: Behavior cloning performance across action chunk size. Success rate per gain setting for the Block Stack task. The preference for compliant and overdamped gain settings is observed when predicting both single actions (a) and action chunks (b) [PITH_FULL_IMAGE:figures/full_fig_p019_25.png] view at source ↗
Figure 26
Figure 26. Figure 26: Behavior cloning performance across action representations. Success rate per gain setting for the Block Stack task. The preference for compliant and overdamped gain settings is observed when predicting both absolute (a) and relative (b) joint position actions. 16 32 64 128 256 512 1024 2048 Proportional Gain (Kp) 1 2 4 8 16 32 64 128 Derivative Gain (Kd) 0.36 0.11 0.08 0.30 0.69 0.31 0.52 0.45 0.44 0.22 0… view at source ↗
Figure 27
Figure 27. Figure 27: Behavior cloning performance across control frequencies. Success rate per gain setting for the Block Stack task. The preference for compliant and overdamped gain settings is observed when predicting actions at 10Hz (a) and 50Hz (b). 30 40 50 60 70 80 90 100 Dataset Size (episodes) 0.0 0.2 0.4 0.6 0.8 1.0 Success Rate CO (Compliant x Overdamped) SO (Stiff x Overdamped) SU (Stiff x Underdamped) CU (Complian… view at source ↗
Figure 28
Figure 28. Figure 28: Offline imitation learning scales more favorably under compliant and overdamped gains. Success rate as a function of dataset size across tasks and robot embodiments. Policies trained with low stiffness and high damping achieve higher success with fewer demonstrations, while stiff or weakly damped controllers exhibit poorer data scaling [PITH_FULL_IMAGE:figures/full_fig_p020_28.png] view at source ↗
Figure 29
Figure 29. Figure 29: Stiff and overdamped gain settings reduce sim2real transferability. The Sim2Real trajectory error (Eq.11) is consistently larger (light blue) in the stiff and overdamped regime (a-c). 32 64 128 256 512 1024 2048 Proportional Gain (Kp) 1 2 4 8 16 32 64 Derivative Gain (Kd) 0.280 0.333 0.170 0.255 0.447 0.170 0.204 0.159 0.247 0.144 0.214 0.192 0.182 0.196 0.119 0.103 0.189 0.132 0.153 0.121 0.146 0.196 0.1… view at source ↗
Figure 30
Figure 30. Figure 30: Stiff and overdamped gains increase Sim2Real NN error. The Sim2Real NN error (Eq.31) is consistently larger (light blue) in the stiff and overdamped regime (a-c) [PITH_FULL_IMAGE:figures/full_fig_p021_30.png] view at source ↗
read the original abstract

Position controllers have become the dominant interface for executing learned manipulation policies. Yet a critical design decision remains understudied: how should we choose controller gains for policy learning? The conventional wisdom is to select gains based on desired task compliance or stiffness. However, this logic breaks down when controllers are paired with state-conditioned policies: effective stiffness emerges from the interplay between learned reactions and control dynamics, not from gains alone. We argue that gain selection should instead be guided by learnability: how amenable different gain settings are to the learning algorithm in use. In this work, we systematically investigate how position controller gains affect three core components of modern robot learning pipelines: behavior cloning, reinforcement learning from scratch, and sim-to-real transfer. Through extensive experiments across multiple tasks and robot embodiments, we find that: (1) behavior cloning benefits from compliant and overdamped gain regimes, (2) reinforcement learning can succeed across all gain regimes given compatible hyperparameter tuning, and (3) sim-to-real transfer is harmed by stiff and overdamped gain regimes. These findings reveal that optimal gain selection depends not on the desired task behavior, but on the learning paradigm employed. Project website: https://younghyopark.me/tune-to-learn

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript claims that position controller gains for learned robot manipulation policies should be selected based on learnability for the specific paradigm (behavior cloning, RL from scratch, or sim-to-real transfer) rather than conventional task-based compliance or stiffness requirements. It supports this via experiments across multiple tasks and robot embodiments showing that (1) behavior cloning benefits from compliant/overdamped gains, (2) RL succeeds across gain regimes when hyperparameters are compatibly tuned, and (3) sim-to-real transfer is harmed by stiff/overdamped regimes, concluding that optimal gains depend on the learning algorithm rather than desired task behavior.

Significance. If the reported patterns hold under broader conditions, the work provides actionable empirical guidance that could improve success rates in robot learning pipelines by decoupling gain choice from task physics. The multi-task, multi-embodiment experimental scope is a strength, offering concrete evidence against purely task-driven gain selection.

major comments (2)
  1. [Experiments (across tasks and embodiments)] The central claim that paradigm dictates gains independently of task behavior rests on the assumption that the tested tasks do not embed varying stiffness requirements; without explicit ablation varying target trajectories or adding stiffness objectives while holding the learning paradigm fixed, the decoupling cannot be isolated from task-specific effects.
  2. [RL experiments and hyperparameter details] For the RL result that it 'can succeed across all gain regimes given compatible hyperparameter tuning,' the manuscript must report the exact search ranges, number of trials, and exclusion criteria used to identify compatible tunings; otherwise the claim reduces to post-hoc selection rather than a general property of the paradigm.
minor comments (2)
  1. [Abstract] Abstract: the terms 'compliant,' 'overdamped,' and 'stiff' should be defined quantitatively (e.g., via damping ratio ranges or specific K_p/K_d values) rather than left qualitative.
  2. [Experimental setup] Provide the full list of tasks, robot platforms, and success metrics in a table for reproducibility; the current description is high-level.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback and for recognizing the multi-task, multi-embodiment scope of our experiments. We address each major comment below with clarifications and proposed revisions.

read point-by-point responses

  1. Referee: [Experiments (across tasks and embodiments)] The central claim that paradigm dictates gains independently of task behavior rests on the assumption that the tested tasks do not embed varying stiffness requirements; without explicit ablation varying target trajectories or adding stiffness objectives while holding the learning paradigm fixed, the decoupling cannot be isolated from task-specific effects.

    Authors: We agree that an explicit ablation holding the learning paradigm fixed while varying target stiffness or trajectory requirements would provide stronger isolation. Our current design instead demonstrates consistent paradigm-dependent patterns across a deliberately diverse set of tasks (pushing, grasping, insertion) and two robot embodiments with differing dynamics. This cross-task consistency is our primary evidence that gain effects are not reducible to task-specific stiffness demands. In the revision we will add a dedicated limitations paragraph acknowledging the absence of a controlled stiffness-objective ablation and will include additional discussion of how task selection was intended to mitigate this concern. revision: partial

  2. Referee: [RL experiments and hyperparameter details] For the RL result that it 'can succeed across all gain regimes given compatible hyperparameter tuning,' the manuscript must report the exact search ranges, number of trials, and exclusion criteria used to identify compatible tunings; otherwise the claim reduces to post-hoc selection rather than a general property of the paradigm.

    Authors: We accept this point. The original manuscript summarized the tuning process at a high level. In the revised version we will add a new subsection (or appendix) that explicitly lists: (i) the hyperparameter search ranges explored for each gain regime, (ii) the total number of trials per regime, and (iii) the quantitative success criteria and exclusion rules applied when declaring a tuning “compatible.” This documentation will make clear that the reported success across regimes rests on systematic search rather than post-hoc selection. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical observations from controlled experiments

full rationale

The paper reports direct experimental measurements of success rates and transfer performance under varying position controller gains for behavior cloning, RL, and sim-to-real pipelines across multiple tasks and robot embodiments. No mathematical derivations, parameter fits, or predictions are presented that reduce to the inputs by construction. Central claims rest on observed patterns (e.g., BC favoring compliant overdamped regimes) rather than self-definitional equations or load-bearing self-citations. The findings are falsifiable via new experiments and do not invoke uniqueness theorems or ansatzes from prior author work.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Empirical study with no mathematical derivation; no free parameters, axioms, or invented entities are introduced or required.

pith-pipeline@v0.9.0 · 5514 in / 1064 out tokens · 30021 ms · 2026-05-13T20:59:56.983675+00:00 · methodology

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Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. A Nonasymptotic Theory of Gain-Dependent Error Dynamics in Behavior Cloning

    cs.RO 2026-04 unverdicted novelty 7.0

    Nonasymptotic analysis shows compliant overdamped PD controllers minimize position error tails in behavior cloning by bounding gain-dependent amplification of sub-Gaussian action errors.

  2. A Nonasymptotic Theory of Gain-Dependent Error Dynamics in Behavior Cloning

    cs.RO 2026-04 unverdicted novelty 7.0

    Nonasymptotic analysis shows sub-Gaussian action errors in behavior cloning propagate through gain-dependent closed-loop dynamics to produce sub-Gaussian position errors whose tail is governed by a proxy matrix and am...

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