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arxiv: 2606.08743 · v1 · pith:BCBJPQJAnew · submitted 2026-06-07 · 💻 cs.RO

Guided Discovery of New Behaviors using Diffusion Policies

Dian Yu , Sebastian Sanokowski , Majid Khadiv This is my paper

Pith reviewed 2026-06-27 18:11 UTC · model grok-4.3

classification 💻 cs.RO
keywords diffusion policiesbehavior discoveryrobot manipulationguiding potentialFeynman-Kac correctorstrajectory optimizationmultimodal action distributions
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The pith

A novel guiding potential with Feynman-Kac correctors steers diffusion policies toward rare but valid trajectories that can be refined and learned.

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

When only a few demonstrations are available, diffusion policies for robot control mostly reproduce the dominant behaviors and miss other valid but uncommon action sequences. This paper establishes that a guiding potential can direct the sampling process to those underrepresented modes while the trajectories stay feasible. The selected samples are then repaired through sampling-based optimization and added to the training set so the policy can generate them. A sympathetic reader would care because the result is a repeatable process for expanding the range of executable behaviors instead of depending on luck or external reinforcement learning loops.

Core claim

The framework combines Feynman-Kac correctors with a novel guiding potential that systematically guides diffusion policy samples towards promising yet underrepresented samples. These trajectories are refined using sampling-based trajectory optimization and reincorporated into the training set to retrain the diffusion policy. Our method effectively mines and repairs novel trajectories, enabling the systematic discovery of diverse and executable behaviors across a range of manipulation environments.

What carries the argument

The novel guiding potential, which identifies and steers diffusion samples toward underrepresented yet feasible trajectories for later refinement.

If this is right

  • Diffusion policies retrained on the refined trajectories generate a wider set of behaviors than the original training data alone.
  • The guided samples avoid the infeasible regions that standard guidance methods produce.
  • Refined trajectories remain executable in the target environments after optimization.
  • The process yields new behaviors consistently across multiple manipulation tasks.

Where Pith is reading between the lines

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

  • The same steering-and-repair loop could be applied to other generative models used for robot behavior generation.
  • Iterating the mining and retraining steps multiple times might produce an expanding library of distinct behaviors over successive rounds.
  • The design suggests that feasibility-preserving guidance can be constructed without relying on reinforcement learning to escape local modes.

Load-bearing premise

The novel guiding potential can systematically identify promising yet underrepresented samples that remain feasible and executable after refinement.

What would settle it

Apply the method to a manipulation task with a known small demonstration set and observe whether any refined trajectories produce new executable behaviors absent from the original demonstrations after retraining.

Figures

Figures reproduced from arXiv: 2606.08743 by Dian Yu, Majid Khadiv, Sebastian Sanokowski.

Figure 1
Figure 1. Figure 1: Overview of the GDNB bootstrapping loop. A Rare-Event sampler proposes frontier [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Representative GDNB-discovered behaviors and real-world replay. (Blue/Cyan) denote common behaviors from the base policy, (orange/red) denote rare behaviors discovered by GDNB. Translucent robot copies indicate temporal rollout snapshots, and colored markers indicate accumulated contact locations. This figure shows key frames and contact/pose summaries. Kitchen (A1–A2): GDNB discovers rare subtask sequence… view at source ↗
Figure 3
Figure 3. Figure 3: , demonstrates that by leveraging the rare-event sampler introduced in Section 3.1 combined with SBTO correction, GDNB successfully recovers the missing modes on this task. 4.2 Rare Behavior Discovery in Robotic Manipulation [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Lennard-Jones-like rare-shell potential. We visualize the normalized score-energy coordinate x = d/d∗ . The shell cost Φ(x) = x −12 − 2x −6 + 1 is minimized at x = 1, corresponding to the target rare shell d = d ∗ . The reward bias B(x) = −Φ(x) therefore attracts samples toward this shell instead of monotonically pushing them toward increasingly large score norms. Green curves show the corresponding deriva… view at source ↗
Figure 5
Figure 5. Figure 5: Condition-wise action densities on Multimodal Agent. Each colored curve is the KDE of sampled actions for one heading condition. The gray curve shows the bimodal reward landscape, and the shaded bands mark the two reward-equivalent optima at a = −0.5 and a = +0.5. The seed-trained baseline is concentrated on the right mode. DPPO preserves the same collapse. REPPO broadens the density around the right well … view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative rare-case diagnostics for remaining benchmarks. (a) End-effector-center trajectories for two phases of Transport, with common rollouts in the left column and rare rollouts in the right column. (b) Base and rare pusher-center trajectories around the T-shaped block in Push-T. (c) Box-frame angle diagnostics for Block Pushing: first-contact angle on the left and post-contact push direction on the … view at source ↗
Figure 7
Figure 7. Figure 7: Task-feature distribution-shift diagnostics across manipulation benchmarks. Each panel shows a one-dimensional Task Feature KDE for a task-related event axis. Blue curves denote rollout samples from the base diffusion policy, orange curves denote rare-sampled rollout candidates, and green curves denote rollout samples from the fine-tuned policy after adaptation. The horizontal coordinate is the base-calibr… view at source ↗
read the original abstract

Diffusion models have become a powerful tool for generative modeling in robotics, with diffusion policies excelling at modeling multimodal action-trajectory distributions. However, when demonstrations are limited, standard sampling often reproduces dominant behaviors while neglecting valid but rare modes, limiting the discovery of novel solutions. Existing approaches, such as guidance methods or combining reinforcement learning with diffusion, either push samples into infeasible regions or struggle to escape local minima, failing to systematically uncover diverse behaviors. To address these challenges, we propose a framework that combines Feynman-Kac correctors with a novel guiding potential that systematically guides diffusion policy samples towards promising yet underrepresented samples. These trajectories are refined using sampling-based trajectory optimization and reincorporated into the training set to retrain the diffusion policy. Our method effectively mines and repairs novel trajectories, enabling the systematic discovery of diverse and executable behaviors. We demonstrate the effectiveness of our framework across a range of manipulation environments, consistently discovering new behaviors.

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

1 major / 0 minor

Summary. The manuscript proposes a framework for discovering novel behaviors with diffusion policies in robotics. It combines Feynman-Kac correctors with a novel guiding potential to steer sampling toward underrepresented yet feasible trajectories, refines those trajectories via sampling-based trajectory optimization, and retrains the diffusion policy on the augmented dataset. The central claim is that this pipeline systematically mines and repairs novel trajectories, yielding diverse and executable behaviors across manipulation environments without the infeasibility or local-minima issues of prior guidance or RL-augmented methods.

Significance. If the empirical claims hold, the approach would offer a principled way to expand the support of learned diffusion policies beyond dominant demonstration modes while preserving executability, which is a recurring limitation in robotic generative modeling with limited data. The explicit use of Feynman-Kac correctors and the guiding potential constitute a potentially reusable technical contribution.

major comments (1)
  1. Abstract: the claim that the method 'consistently discover[s] new behaviors' across manipulation environments is presented without any quantitative results, success rates, diversity metrics, error analysis, or validation of the guiding potential, so the central empirical claim cannot be assessed from the provided description.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their thoughtful review and the opportunity to clarify our work. We address the single major comment below.

read point-by-point responses

  1. Referee: Abstract: the claim that the method 'consistently discover[s] new behaviors' across manipulation environments is presented without any quantitative results, success rates, diversity metrics, error analysis, or validation of the guiding potential, so the central empirical claim cannot be assessed from the provided description.

    Authors: We agree that the abstract, as written, states the empirical claim without supporting quantitative details. The body of the manuscript reports success rates, diversity metrics (e.g., mode coverage and trajectory variance), error analyses, and ablation studies validating the guiding potential across the manipulation tasks. To make the central claim directly assessable from the abstract, we will revise it in the next version to include concise quantitative highlights (e.g., average success-rate improvements and diversity gains) while preserving the overall length and readability. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper describes an empirical methodological pipeline (Feynman-Kac correctors + novel guiding potential + sampling-based refinement + retraining) whose central claims are supported by experimental results across manipulation tasks rather than by any derivation that reduces to fitted inputs or self-citations by construction. No equations, self-definitional steps, or load-bearing self-citations appear in the provided abstract or description; the work is self-contained against external benchmarks and does not rename known results or smuggle ansatzes via citation.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no explicit free parameters, axioms, or invented entities; the guiding potential is described as novel but without details on its form or any fitted values.

pith-pipeline@v0.9.1-grok · 5683 in / 1097 out tokens · 18182 ms · 2026-06-27T18:11:02.450159+00:00 · methodology

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