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arxiv: 2606.10818 · v1 · pith:Q4IG4BMDnew · submitted 2026-06-09 · 💻 cs.RO · cs.CV

IMPACT: Learning Internal-Model Predictive Control for Forceful Robotic Manipulation

Jiawei Gao , Chaoqi Liu , Peilin Wu , Haonan Chen , Yilun Du This is my paper

Pith reviewed 2026-06-27 13:02 UTC · model grok-4.3

classification 💻 cs.RO cs.CV
keywords robotic manipulationpredictive controlinternal modelforceful interactiongeneralizationimpedance controlenergy efficiencysafety
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The pith

A learned internal model enables predictive control of forceful robot manipulations without force sensors or per-object tuning.

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

The paper argues that forceful robotic tasks, such as handling tools of different weights or contact-rich wiping, can be solved by decoupling high-level task planning from low-level predictive control that relies on an internal model learned from data. This approach replaces both the generalization failures of imitation policies tracked by impedance controllers and the hardware demands of explicit force sensing. If correct, the result is higher task success across weight variations, reduced energy use, and safer operation in real-world settings.

Core claim

The IMPACT framework decouples forceful robotic manipulation into task-planning and internal-model-based predictive control. An internal model learned from data captures interaction dynamics sufficiently to generate predictions that replace explicit force/torque sensing and post-hoc tuning for each new weight, producing higher success rates, improved generalization to unseen object weights, and gains in safety and energy efficiency.

What carries the argument

The decoupling of task planning from internal-model-based predictive control, where the learned internal model supplies the dynamics predictions needed for forceful contact.

If this is right

  • Higher success rates on forceful tasks such as tool use and table wiping.
  • Generalization to object weights absent from training data without retraining or manual tuning.
  • Lower energy consumption and improved safety margins during contact-rich interactions.
  • Elimination of wrist force/torque or tactile sensors from the control architecture.

Where Pith is reading between the lines

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

  • The separation of planning from model-based prediction may reduce overall system complexity for deployment in factories or homes.
  • The same internal-model approach could apply to other physical-interaction domains such as locomotion over uneven terrain.
  • Performance gains might persist under additional disturbances like friction changes or external pushes not tested in the original experiments.

Load-bearing premise

A data-learned internal model can capture the relevant dynamics of forceful interactions well enough to support reliable predictive control.

What would settle it

An experiment that applies the same tasks with varying unseen weights to both the internal-model controller and a standard impedance baseline and finds no difference in success rate or generalization.

Figures

Figures reproduced from arXiv: 2606.10818 by Chaoqi Liu, Haonan Chen, Jiawei Gao, Peilin Wu, Yilun Du.

Figure 1
Figure 1. Figure 1: We demonstrate the effectiveness of our framework across a variety of forceful manipu [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Control diagram of our frame￾work. A task-space impedance controller pro￾vides feedback regulation based on tracking error, while an internal model learns online from joint measurements to generate feedforward torque commands that compensate for persistent environment-induced interaction forces. General-purpose robot learning systems hold the promise of enabling robots to acquire a wide range of manipulati… view at source ↗
Figure 3
Figure 3. Figure 3: Overview of IMPACT and comparison with implicit force control method. (a) In implicit force control, forces are generated implicitly by the policy through producing virtual target trajectories that induce tracking errors, which are converted into interaction forces by a low-level impedance controller. (b) In IMPACT, the controller generates desired interaction forces: an internal model predicts contact and… view at source ↗
Figure 4
Figure 4. Figure 4: Simulation Experiments Setup. We set up tasks in the MuJoCo simulator for con￾trolled evaluation of the proposed framework against baseline methods. The teleoperation, data￾postprocessing, and policy training pipelines are consistent with the real-robot experiments, ensur￾ing a fair comparison. 3.1 Experiments Setup [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Real-World Setup. We use a Nin￾tendo Switch Joy-Con to teleoperate the Franka FR3 robot to pick up dumbbells weighing 2.5 kg and 5 kg. The teleoperation data are collected at 30 Hz and used to train the policies. Real-World Experiments. We conduct real￾world experiments using a 7-DOF Franka FR3 manipulator operating in a tabletop workspace monitored by two fixed-base RGB-D cameras ( [PITH_FULL_IMAGE:figur… view at source ↗
Figure 6
Figure 6. Figure 6: Simulation Benchmarking. Evaluation of task success rates in the MuJoCo simulation benchmark across varying object masses (0–10 kg). x axis denotes the object mass, z axis denotes the task success rate, and y axis represents different methods. While Vanilla DP (trained on 0.2 kg) fails to generalize and Augmented DP (trained on 0.1–8.0 kg) degrades at high payload (> 8 kg), IMPACT maintains superior perfor… view at source ↗
Figure 7
Figure 7. Figure 7: Visualization of the key metrics dur￾ing the task protocol. We visualize the inter￾nal model behavior across three phases: (A) ini￾tial weight learning via surprise gate activation, (B) zero-delay feedforward compensation for a known load, and (C) gate reactivation for unex￾pected mass increase (2.5 kg to 5 kg). To understand the underlying mechanisms that contribute to the superior performance of IMPACT, … view at source ↗
Figure 8
Figure 8. Figure 8: Example visualization of one real-world episode. 3D visualization of applied wrench, estimated wrench, and internal weight estimates. IMPACT demonstrates successful mass identi￾fication in Window 1 (2.5 kg) and rapid re-adaptation in Window 2 (5 kg) upon detecting load discrepancies. Also, we compare the pose tracking performance of the baseline impedance controller and IMPACT in the real-world experiments… view at source ↗
Figure 9
Figure 9. Figure 9: Baseline Impedance Control. Significant steady-state error is observed across all phases due to the lack of load compensation. −0.050 −0.025 0.000 0.025 0.050 0.075 0.100 0.125 0.150 Pose error z (m) −0.050 −0.025 0.000 0.025 0.050 0.075 0.100 0.125 0.150 Pose error z (m) 0 10 20 30 40 Time (s) 0 10 20 30 40 50 60 Force (N) (a) Phase A Applied Wrench Pose Error (z) 0 20 40 60 80 100 120 Time (s) 0 10 20 30… view at source ↗
Figure 10
Figure 10. Figure 10: IMPACT Performance. The feedforward wrench effectively cancels external loads, maintaining pose error within the noise floor (red region). 15 [PITH_FULL_IMAGE:figures/full_fig_p015_10.png] view at source ↗
read the original abstract

Real-world robotic manipulation tasks often involve forceful interactions with the environment, such as using tools of varying weights, transporting objects with different masses, and performing contact-rich tasks like table wiping. Previous learning-based approaches typically employ imitation learning policies that output target end-effector poses tracked by low-level impedance controllers. In these systems, forceful interactions are either implicitly realized through steady-state tracking errors or explicitly commanded using wrist force/torque or tactile sensors. However, implicit approaches generalize poorly across object weights, while explicit approaches require specialized hardware and increase system complexity. In this work, we propose IMPACT, a framework that decouples these forceful tasks into task-planning and internal-model-based predictive control. Extensive simulation and real-world experiments demonstrate that the proposed framework achieves higher success rates and improved generalization to unseen object weights, as well as better safety and energy efficiency.

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

0 major / 3 minor

Summary. The paper introduces IMPACT, a framework for forceful robotic manipulation that learns an internal model to enable predictive control, decoupling it from task planning. This avoids reliance on force/torque sensors or per-object tuning. Through simulation and real-world experiments, it demonstrates higher success rates, better generalization to unseen object weights, improved safety, and energy efficiency compared to prior imitation learning approaches with impedance controllers.

Significance. If the claims hold, this work is significant for advancing learning-based methods in contact-rich robotic tasks. It provides a way to handle varying dynamics without additional hardware. The combination of simulation and real experiments, along with the decoupling approach, offers a practical contribution. Strengths include the experimental validation supporting the generalization claims.

minor comments (3)
  1. [Abstract] The abstract claims higher success rates and improved generalization but does not provide any quantitative results or specific comparisons. Including key metrics would make the summary more informative.
  2. [Experiments] Ensure that all experimental setups, including the range of unseen weights tested and the exact baselines used, are described with sufficient detail for replication.
  3. Check for consistency in notation between the method description and the results tables.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive assessment of our IMPACT framework and the recommendation for minor revision. The summary accurately reflects the paper's contributions regarding decoupling task planning from internal-model predictive control, along with the reported gains in success rate, generalization, safety, and efficiency.

Circularity Check

0 steps flagged

No significant circularity

full rationale

The provided abstract and context describe a learning-based framework for robotic control without any equations, fitted parameters presented as predictions, or self-citation chains. No derivation steps are visible that reduce by construction to inputs. The central claim rests on empirical simulation and real-world results for success rates and generalization, which are externally falsifiable and not internally forced by definition or renaming. This is the expected self-contained case for a methods paper.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Review performed on abstract only; no free parameters, axioms, or invented entities are stated in the provided text.

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discussion (0)

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Reference graph

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