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Energy-Regularized Imitation Learning for Force- and Work-Aware Robotic Manipulation
Toshiki Otani, Hiromu Taketsugu, Norimichi Ukita
TL;DR
Visual imitation-learning policies can complete tasks without explicitly accounting for the physical effort of their motions, especially in contact-rich manipulation. This paper defines joint-space mechanical work, learns a differentiable predictor from robot states and actions, and uses it to regularize a pretrained policy. Across 12 RLBench tasks, fine-tuning reduces average work from 208.8 J to 204.4 J while mean success rises from 86.2% to 86.9%.
Problem
Imitation-learning objectives typically emphasize manipulation accuracy and task success without explicitly evaluating the physical effort required by executed motions.
Method
The method predicts simulator-derived joint-space mechanical work from robot states and actions, then uses the frozen predictor as a differentiable regularizer for fine-tuning a pretrained policy.
Results
Across 12 RLBench tasks, average mechanical work decreases from 208.8 J to 204.4 J while mean task success increases from 86.2% to 86.9%.
Takeaways & Limitations
Work-aware policy optimization can reduce physically inefficient motion without requiring an explicit differentiable simulator.
Takeaways & Limitations
The joint-space work proxy measures robot mechanical effort but does not explicitly account for energy transferred to objects through contact.
Abstract
from arXiv · showhide
This paper studies energy-aware manipulation as a physically grounded learning problem. We define a joint-space mechanical-work proxy from joint torque and angular displacement, and train a differentiable energy predictor that estimates this work from robot states and actions. The predictor converts a non-differentiable simulator-side physical quantity into a differentiable regularizer for fine-tuning a pretrained manipulation policy. We instantiate the framework with RVT-2 on RLBench and evaluate 12 manipulation tasks involving object contact, articulated motion, placement, pushing, and sweeping. The proposed fine-tuning reduces the average mechanical work from 208.8J to 204.4J (i.e., 2.1% reduction), while the mean task success rate also increases slightly from 86.2% to 86.9%. These results show that work-aware policy optimization can suppress physically inefficient motion without requiring an explicit differentiable dynamics model.
1 Introduction
Imitation-learning policies can complete manipulation tasks through physically inefficient motion, while mechanical work offers a force-grounded training signal. The paper addresses nondifferentiable simulator measurements with a learned energy predictor used to regularize a pretrained policy.
- 1 Introduction: A policy may succeed while using redundant accelerations, excessive joint motion, or avoidable environmental contact because imitation objectives typically emphasize action accuracy.This motivates evaluating physical effort alongside task completion.
- 1 Introduction: Mechanical work captures robot effort through force- and torque-related motion, making it relevant to contact-rich articulated manipulation beyond task success alone.The paper uses work as an additional policy-learning signal.
- 1 Introduction: Simulator-generated torque and contact computations are nondifferentiable and difficult to use directly as a neural policy loss.The simulator is outside the neural computation graph, and real-robot dynamics and contact states are difficult to identify.
- 1 Introduction: The method accumulates joint-space work, trains an energy predictor from states and actions, and uses it to fine-tune a pretrained imitation policy.The predictor supplies a differentiable approximation of simulator-side work.
- 1 Introduction: The framework evaluates 12 RLBench tasks and reports a 2.1% average mechanical-work reduction without degrading average success rate.This is the paper’s stated overall evaluation result.
2 Related Work
Prior manipulation learning methods mainly optimize action prediction and task success, while force- and work-aware control traditionally relies more on dynamics models and trajectory optimization. Surrogate physical models provide a route to differentiable optimization when simulator quantities are otherwise inaccessible.
- 2 Related Work: Classical approaches reduce control cost or mechanical effort using dynamics models, torque limits, and trajectory optimization, whereas learned contact-rich manipulation often lacks exact dynamics.RLBench and Meta-World mainly evaluate task success, motivating an additional work target.
- 2 Related Work: Neural surrogate models approximate simulator-computed physical quantities and provide gradients for policy optimization without simulating full contact dynamics.This paper applies that idea to predicting manipulator work from robot states and actions.
3 Method
The method defines joint-space mechanical work from simulator torque and angular displacement, then learns a differentiable predictor to regularize fine-tuning of a pretrained manipulation policy.
- 3.1 Mechanical Work as the Energy Target: Joint-space work is computed from torque and angular displacement, aggregated over simulator steps belonging to each higher-level policy action.The proxy measures absolute manipulator effort rather than net work transferred to objects or actuator electrical energy.
- 3.1 Mechanical Work as the Energy Target: The work target is physically meaningful but cannot directly provide policy gradients because simulator torque, contacts, collision handling, and low-level control are nondifferentiable.The learned surrogate addresses this simulator-to-policy gradient gap without requiring the simulator to be part of the neural computation graph.
- 3.2 Differentiable Energy Predictor: The energy predictor estimates action-level work from robot state and action inputs, using simulator-computed work labels for supervision.Its inputs include robot state, the key action, and a step-count feature; simulator torque is used only to create labels.
- 3.3 Energy-Regularized Fine-Tuning: A frozen predictor supplies a normalized work penalty while only the manipulation policy is updated alongside the original imitation loss.The total objective combines task imitation with λ-weighted predicted energy, using fixed training-set minimum and maximum work values for normalization.
- 3.3 Energy-Regularized Fine-Tuning: The fine-tuning tradeoff is controlled by λ, with λ = 10 used as the main setting and excessive weights risking conservative actions or avoided contact.The predictor is frozen so the policy must change its actions rather than the physical model to reduce predicted work.
4 Experiments
Across 12 RLBench tasks, energy-regularized fine-tuning reduced average mechanical work while slightly increasing success, with filtering and moderate energy weighting affecting downstream performance.
- 4.3 Energy-Regularized Fine-Tuning: 2.1%: Energy-regularized RVT-2 reduced average work from 208.8 J to 204.4 J while increasing average success from 86.2% to 86.9%.The comparison uses λ = 10 and averages work over successful episodes.
- 4.2 Energy Predictor Accuracy: 2.40 J: The energy predictor achieved 2.40 J average MAE and 7.02% average relative error across 12 tasks.Errors were smaller for button interaction, sweeping, and simple reaching, and larger for articulated-object and long-horizon placement tasks.
- 4.4 Ablation Studies: 2.40 J: HDBSCAN filtering improved predictor MAE from 2.80 J to 2.40 J and enabled fine-tuning to reduce mean work to 204.4 J, unlike the unfiltered predictor’s 214.8 J.The ablation supports estimating energy from representative successful behavior rather than all successful rollouts indiscriminately.
- 4.4 Ablation Studies: 2.30 J: Adding internal RVT-2 features lowered predictor MAE from 2.40 J to 2.30 J, but downstream fine-tuning produced 207.8 J mean work.The result indicates predictor accuracy alone does not determine regularizer effectiveness; gradients must align with controllable action changes.
- 4.4 Ablation Studies: λ = 10: Moderate weighting gave the clearest average work reduction, while excessive weights harmed success in contact-rich insertion and articulated-object tasks.The paper reports λ = 5 and λ = 7 as often safer for success-critical tasks.
5 Discussion
The method targets physical inefficiency using work dependent on joint torque and motion, while avoiding backpropagation through contact simulation. Its current metric measures robot mechanical effort rather than exact energy transferred to objects, motivating contact-aware extensions.
- 5 Discussion: The method leaves the visual encoder unchanged, adding physically grounded supervision to the action prediction head for compatibility with visual manipulation policies.This makes the approach complementary to visual representation learning.
- 5 Discussion: Work-based regularization captures inefficiency that smoothness penalties can miss during pushing or unnecessary torque application.The method uses joint torque and motion rather than a purely kinematic smoothness penalty.
- 5 Discussion: The predictor is trained from physics simulation and used as a differentiable energy term, avoiding backpropagation through contact simulation.
- 5 Discussion: The joint-space work metric measures robot mechanical effort rather than exact energy transferred to the object because it does not explicitly gate object contact.The paper proposes combining joint-space work with contact detection and force/torque sensing.
- 5 Discussion: Task-specific contact requirements constrain energy regularization because insertion and stacking may require high work to satisfy geometric constraints.The paper suggests contact-aware losses, task-conditioned weights, and energy-predictor uncertainty estimates.
6 Conclusion
The paper presents energy-regularized imitation learning using a learned predictor of joint-space mechanical work to fine-tune a pretrained policy. Across 12 RLBench tasks, it modestly reduces average work while slightly increasing mean success rate.
- 6 Conclusion: Average mechanical work decreases from 208.8 J to 204.4 J, while mean success rate increases from 86.2% to 86.9% across 12 RLBench tasks.The paper characterizes the reduction as modest and the energy term as a conservative regularizer for a pretrained high-success policy.
- 6 Conclusion: The method defines a joint-space work proxy from torque and angular displacement, learns a differentiable predictor, and uses it to fine-tune a pretrained policy.The frozen predictor supplies an additional loss without requiring a differentiable simulator.