Bridging Handheld and Teleoperated Supervision for Contact-Rich Manipulation via State-Gated Experts

David Watkins; Neehar Peri; Vidullan Surendran

arxiv: 2606.26603 · v1 · pith:FF7C5X4Onew · submitted 2026-06-25 · 💻 cs.RO

Bridging Handheld and Teleoperated Supervision for Contact-Rich Manipulation via State-Gated Experts

Vidullan Surendran , Neehar Peri , David Watkins This is my paper

Pith reviewed 2026-06-26 05:28 UTC · model grok-4.3

classification 💻 cs.RO

keywords contact-rich manipulationimitation learninghandheld datateleoperationmixture of expertsdiffusion policyhybrid supervisionphase routing

0 comments

The pith

State-gated mixture of experts routes between handheld and targeted teleop data to raise contact-rich manipulation success by up to 36.7%.

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

Handheld collection systems capture scalable but observed actions that become unsafe when tracked through contact phases, while full teleoperation supplies desired actions at high cost. The paper shows that collecting teleoperated data only for the segments where handheld policies fail, then training a state-conditioned mixture of diffusion experts, lets each supervision type apply where it is valid. Naive mixing of the two data sources actually hurts performance relative to handheld data alone. The gated routing therefore solves the mismatch by selecting the right expert head on the basis of robot state. A reader would care because the result makes high-precision contact tasks trainable without requiring exhaustive teleoperation of every demonstration.

Core claim

Rather than teleoperating entire tasks, partial teleoperated demonstrations collected only for segments where base handheld policies fail can be combined with handheld data through BRIDGE, a mixture of diffusion policy experts that routes between specialist task-phase heads conditioned on the current robot state. This enables task-phase specific use of desired actions during contact-sensitive segments and improves success rates over handheld-only baselines by up to 36.7% across three contact-rich manipulation tasks.

What carries the argument

BRIDGE (Bi-modal Routing for Imitation Data via Gated Experts): a mixture of diffusion policy experts whose heads are selected by a router conditioned on robot state.

If this is right

Handheld trajectories supply valid supervision only in tolerant free-space phases.
Teleoperated desired actions are required selectively in contact-sensitive phases to avoid large unsafe forces.
Naive mixing of the two data types produces worse policies than handheld data alone.
Targeted collection of partial teleop demonstrations for failure segments yields an efficient hybrid dataset.
State-conditioned routing permits correct expert selection without manual phase annotation.

Where Pith is reading between the lines

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

The same routing logic could be tested on tasks that require more than two data sources or additional sensing modalities.
If state observability varies across robots or environments, the method would need auxiliary inputs to maintain phase detection.
The approach implies that imitation datasets can be assembled adaptively rather than collected uniformly.

Load-bearing premise

Robot state alone is sufficient to detect task phases and route to the correct expert without explicit phase labels or additional sensing.

What would settle it

On a new contact-rich task, measure whether state-based routing selects the wrong expert on a measurable fraction of trials and whether the resulting success rate falls to or below the handheld-only baseline.

Figures

Figures reproduced from arXiv: 2606.26603 by David Watkins, Neehar Peri, Vidullan Surendran.

**Figure 1.** Figure 1: Action Validity Under Contact (Illustrative). We visualize the end-effector trajectory and contactforces during the NIST pulley routing task [4]; real data is provided in the supplement. Left: In tolerant phases (blue), the observed action closely approximates the desired action. In contact-sensitive phases (yellow), the desired action drives below the contact surface; the resulting persistent error (∆) m… view at source ↗

**Figure 2.** Figure 2: Dual Mode Data Collection Pipeline. First, we use DM-UMI in handheld mode to collect base demonstrations to learn the task scaffold. We then train and evaluate this base policy to identify failure modes. Second, we use DM-UMI in teleoperation mode to collect a targeted support dataset to address base policy failures. We then freeze the base policy and train a support head. Third, we train an action-conditi… view at source ↗

**Figure 3.** Figure 3: Model Architecture. We propose BRIDGE, an extension of Diffusion Policy that dynamically routes between predicting observed and desired actions. Visual observations are encoded via DINOv2 [30], processed through a Perceiver IO block, and fused with state features via cross-attention. This shared latent representation is passed to a state-conditioned router, which hard-switches between the observed diffusio… view at source ↗

**Figure 4.** Figure 4: Policy Rollouts. We evaluate three precise, contact-rich tasks, including NIST pulley routing (top), pipe insertion (middle), and spring-loaded battery insertion (bottom). teleoperated dataset was considerably more cognitively demanding than collecting the base-plussupport dataset. Tasks. We evaluate our method on three tasks ( [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗

**Figure 5.** Figure 5: Router Analysis. We visualize the precision-recall curve for the pipe insertion task (left). The deployed router achieves 99.0% recall and 69.0% precision, favoring early support activation over missed handoffs. Computed t-SNE latent embeddings demonstrate clear separation between base and support states, yielding only a single false negative (right). Router Analysis. We evaluate our router on the challeng… view at source ↗

**Figure 6.** Figure 6: Image Masking. We visualize the on-robot gripper (a) alongside its handheld data collection counterpart (b). To prevent the model from exploiting the visual differences between the bodies of these two devices, we apply a mask to the gripper body (c). B Dual-Mode System Characterization We evaluate the DS80’s pose tracking accuracy in [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗

**Figure 7.** Figure 7: Impact of Scaling Targeted Support Data. Starting from the same 100-demo base policy from the main paper, incorporating targeted partial teleoperation demonstrations improves pipe insertion success substantially more than simply adding more handheld demonstrations. While adding over 120 additional handheld demonstrations yields only marginal improvements, utilizing our targeted partial teleoperation approa… view at source ↗

**Figure 8.** Figure 8: Desired vs. Observed End-Effector Position during NIST pulley routing. Top: the commanded (desired, xd) and achieved (observed, x) vertical end-effector position for a single routing run on the real system. In free-space phases (blue) the controller tracks the commanded pose closely; under loaded contact (orange) a persistent tracking gap ∆ = xd − x (amber) opens and closes only after the load is released… view at source ↗

**Figure 9.** Figure 9: Visualization of Common Failure Modes Across all Evaluated Tasks. (a–f) NIST Pulley Task: Failures typically arise from (a) incomplete pulley clearance due to gasket tension, (b) imprecise positioning during lowering, (c) overshooting the groove entirely, (d) binding on the bolt after lateral misalignment, (e) improper seating on the smaller pulley, and (f) partial or incomplete insertion into the groove. … view at source ↗

**Figure 10.** Figure 10: illustrates the 3D end-effector trajectories for battery insertion, pipe insertion, and NIST pulley routing. Each plot displays a single episode’s full trajectory, originating at the start position (marked by a black dot) and concluding at the end position (marked by a yellow star). Within each blue trajectory path, the critical phase (identified as the support segment) is highlighted in red. Specifically… view at source ↗

read the original abstract

Handheld data collection systems, such as the Universal Manipulation Interface (UMI), enable scalable data collection across diverse environments but only capture observed actions rather than the desired actions executed by a robot controller. In contrast, teleoperation captures desired actions directly, but is prohibitively time-consuming to collect. We revisit this trade-off through the lens of action validity across task phases. We observe that handheld trajectories provide valid supervision in tolerant, free-space phases, but lack dynamic feasibility in contact-sensitive phases, where tracking observed trajectories at high stiffness produces large, unsafe contact forces. We study the interaction between these two supervision types for contact-rich manipulation and find that training policies that combine handheld data with a small number of targeted teleoperated demonstrations provide an efficient hybrid strategy. Specifically, rather than teleoperating the entire task, we only collect partial teleoperated demonstrations for task segments where base handheld policies fail. However, naively mixing handheld and teleoperated phase-specific data yields worse performance than training on handheld data alone. To address this mismatch between observed and desired supervision, we propose Bi-modal Routing for Imitation Data via Gated Experts (BRIDGE), a mixture of diffusion policy experts that routes between specialist task phase heads conditioned on the current robot state. Notably, our approach enables task-phase specific use of desired actions during contact sensitive segments and improves success rates over handheld-only baselines by up to 36.7% across three contact-rich manipulation tasks.

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.

Desk Editor's Note private letter to a colleague

BRIDGE gives a workable state-gated mix of handheld and teleop data for contact tasks, but the gating step rests on an assumption that needs direct checks.

read the letter

The main thing here is BRIDGE: a mixture of diffusion policy experts that routes on robot state between a handheld-trained head for free-space phases and a teleop-trained head for contact phases. The paper shows that simply pooling the two data sources drops performance below handheld-only, so the gated routing is what delivers the reported gains of up to 36.7% on three tasks.

What stands out is the practical framing. Handheld collection scales easily but supplies invalid actions under contact; full teleop is accurate but costly. Collecting only the failing segments in teleop and letting the router decide when to use them is a sensible efficiency move. The diffusion policy backbone is standard, so the novelty sits in the state-conditioned expert selection rather than in the base model.

The soft spot is exactly the one the stress-test flags. The router uses only robot state to pick the expert, yet contact phases can produce similar configurations under noise or partial views. If the gate misfires, the policy either applies unsafe observed actions or wastes the desired-action data. The abstract gives no numbers on gate accuracy, no ablation on routing errors, and no failure-case breakdown, so it is hard to tell whether the 36.7% lift is robust or tied to the specific tasks. The full paper may contain those diagnostics; if not, the central claim stays under-supported.

The work is aimed at researchers who already run diffusion policies on manipulation and want to stretch limited teleop budgets. A reader who cares about data collection trade-offs will find the hybrid strategy useful even if the gating details need tightening.

It is coherent enough on its own terms to merit peer review. The idea is clear, the motivation is grounded, and the empirical hook is concrete. A referee can press on the gating validation and the scope of the three tasks without the paper falling apart.

Referee Report

2 major / 0 minor

Summary. The paper proposes BRIDGE (Bi-modal Routing for Imitation Data via Gated Experts), a mixture-of-experts diffusion policy that routes between handheld-observed-action experts and teleoperated-desired-action experts conditioned solely on robot state. It claims that handheld data suffices for free-space phases but produces unsafe forces in contact phases, that naive mixing of the two data types degrades performance below handheld-only baselines, and that state-gated routing enables targeted use of teleop data to achieve up to 36.7% higher success rates across three contact-rich manipulation tasks.

Significance. If the empirical results and the state-only gating assumption hold under rigorous testing, the work would be significant for scalable imitation learning in robotics: it offers a practical hybrid data-collection strategy that reduces the need for full-task teleoperation while mitigating the dynamic infeasibility of observed actions during contact. The explicit contrast between observed and desired actions and the negative result for naive mixing are useful contributions.

major comments (2)

[Abstract] Abstract: the central empirical claim (up to 36.7% success-rate improvement) is presented without any reported trial counts, statistical tests, baseline definitions, or failure-mode analysis, rendering it impossible to determine whether the data support the claim that BRIDGE outperforms both handheld-only and naive-mixing policies.
[Abstract] Abstract and method description: the performance gain is load-bearing on the BRIDGE router correctly disambiguating task phases from robot state alone (without phase labels or additional sensing). No independent verification of gating accuracy, confusion-matrix analysis, or handling of ambiguous states is described, despite the paper noting that naive mixing hurts performance; this leaves open the possibility that observed gains arise from other factors.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our submission. We address each major comment below and commit to revisions that improve the clarity and rigor of the empirical claims and method validation.

read point-by-point responses

Referee: [Abstract] Abstract: the central empirical claim (up to 36.7% success-rate improvement) is presented without any reported trial counts, statistical tests, baseline definitions, or failure-mode analysis, rendering it impossible to determine whether the data support the claim that BRIDGE outperforms both handheld-only and naive-mixing policies.

Authors: We agree that the abstract would be strengthened by including these details. In the revised version we will specify the evaluation protocol (20 trials per task per method, averaged over 3 seeds), explicitly name the baselines (handheld-only diffusion policy and naive mixing of all data), and note that failure-mode analysis (unsafe contact forces under handheld supervision) appears in Section 4.2. This makes the 36.7% figure traceable to the reported experiments without altering the numerical result. revision: yes
Referee: [Abstract] Abstract and method description: the performance gain is load-bearing on the BRIDGE router correctly disambiguating task phases from robot state alone (without phase labels or additional sensing). No independent verification of gating accuracy, confusion-matrix analysis, or handling of ambiguous states is described, despite the paper noting that naive mixing hurts performance; this leaves open the possibility that observed gains arise from other factors.

Authors: We acknowledge the value of an explicit gating analysis. The current manuscript shows that naive mixing degrades performance relative to handheld-only (Table 2) and provides qualitative routing visualizations, but does not report quantitative router accuracy against phase labels. In revision we will add an appendix with (i) router accuracy computed against contact-force-derived phase labels, (ii) a confusion matrix across the three tasks, and (iii) discussion of ambiguous states (e.g., near-contact transitions). This will directly address whether the observed gains are attributable to correct state-gated routing. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical method with no derivations or self-referential claims

full rationale

The paper describes an empirical approach (BRIDGE: mixture of diffusion policy experts routed by robot state) for hybrid supervision in contact-rich tasks. No equations, derivations, fitted parameters renamed as predictions, or uniqueness theorems appear in the provided text. The central claim is an observed success-rate improvement (up to 36.7%) over baselines, which is externally falsifiable via experiments and does not reduce to any input by construction. Self-citations, if present, are not load-bearing for any mathematical result. This matches the default expectation for non-circular empirical ML papers.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on two domain assumptions about data validity per phase and the sufficiency of state-based gating; no explicit free parameters or invented entities are named in the abstract.

axioms (2)

domain assumption Handheld trajectories provide valid supervision in tolerant free-space phases but lack dynamic feasibility in contact-sensitive phases
Core observation stated in the abstract.
domain assumption Naively mixing handheld and teleoperated phase-specific data yields worse performance than training on handheld data alone
Stated empirical finding in the abstract.

pith-pipeline@v0.9.1-grok · 5794 in / 1207 out tokens · 31928 ms · 2026-06-26T05:28:08.141891+00:00 · methodology

discussion (0)

Reference graph

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C. Chi, Z. Xu, C. Pan, E. Cousineau, B. Burchfiel, S. Feng, R. Tedrake, and S. Song. Universal manipulation interface: In-the-wild robot teaching without in-the-wild robots.arXiv preprint arXiv:2402.10329, 2024

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