SeeTraceAct: Visibility-Aware Latent Planning from Cross-Embodiment Demonstration Videos

Chris Dongjoo Kim; Dieter Fox; Jaehyeon Son; Jaemin Cho; Jeremiah Coholich; Jinhoo Kim; Junhyun Kim; Kyle Kam; Seok Joon Kim; Zsolt Kira

arxiv: 2606.02745 · v1 · pith:QI3N4ZZ2new · submitted 2026-06-01 · 💻 cs.RO · cs.LG

SeeTraceAct: Visibility-Aware Latent Planning from Cross-Embodiment Demonstration Videos

Jaehyeon Son , Junhyun Kim , Kyle Kam , Jeremiah Coholich , Seok Joon Kim , Jinhoo Kim , Chris Dongjoo Kim , Jaemin Cho

show 2 more authors

Dieter Fox Zsolt Kira

This is my paper

Pith reviewed 2026-06-28 13:59 UTC · model grok-4.3

classification 💻 cs.RO cs.LG

keywords SeeTraceActdemo-conditioned VLAsend-effector trace predictionvisibility-aware planningcross-embodiment demonstrationsRoboCasa-DCspatial groundingone-shot robot learning

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The pith

SeeTraceAct improves one-shot demo-conditioned VLAs by predicting visibility-aware future end-effector traces for precise spatial grounding.

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

The paper studies one-shot demo-conditioned vision-language-action models, where a robot policy learns from a single video demonstration of an unseen task. Existing end-to-end methods often fail when tasks require accurately localizing small target regions. SeeTraceAct addresses this by adding visibility-aware prediction of future end-effector traces to encourage better spatial grounding in latent planning. The authors also release RoboCasa-DC, a cross-embodiment dataset pairing humanoid demonstration videos with robot episodes. Experiments on this benchmark and a real-world Franka Panda setup show higher success rates than baselines.

Core claim

SeeTraceAct is a demo-conditioned VLA framework that encourages precise spatial grounding through visibility-aware prediction of future end-effector traces, outperforming prior end-to-end approaches on tasks that require localizing small targets when conditioned on a single cross-embodiment demonstration video.

What carries the argument

Visibility-aware prediction of future end-effector traces, which supplies an auxiliary supervision signal for spatial grounding inside the latent planning process of the VLA.

If this is right

SeeTraceAct records the highest success rate in every one of the four RoboCasa-DC evaluation settings.
Conditioning a real Franka Panda arm on human demonstration videos raises average success by 12.5 percentage points.
The method supports one-shot adaptation to new tasks without collecting task-specific teleoperation data.
RoboCasa-DC provides a reproducible testbed for cross-embodiment demo-conditioned policies.

Where Pith is reading between the lines

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

Adding explicit trace prediction could generalize to multi-step manipulation sequences where cumulative localization errors compound.
The visibility-aware auxiliary loss might reduce reliance on large volumes of embodiment-specific data by transferring spatial cues across robots and humans.
Similar trace-based grounding signals could be tested in other latent planners that currently rely solely on image or language conditioning.

Load-bearing premise

The primary shortcoming of existing end-to-end demo-conditioned VLAs is insufficient precise spatial grounding that can be fixed by adding visibility-aware future end-effector trace prediction.

What would settle it

A controlled comparison on the RoboCasa-DC tasks where SeeTraceAct shows no improvement over a baseline VLA that lacks the trace-prediction head, or where another spatial-grounding technique without traces matches or exceeds its success rates.

Figures

Figures reproduced from arXiv: 2606.02745 by Chris Dongjoo Kim, Dieter Fox, Jaehyeon Son, Jaemin Cho, Jeremiah Coholich, Jinhoo Kim, Junhyun Kim, Kyle Kam, Seok Joon Kim, Zsolt Kira.

**Figure 1.** Figure 1: Overview of SEETRACEACT. Given a demonstration video, current camera views, and a language instruction, the policy encodes task-relevant information into a visual latent plan (SEE). During training, the policy predicts future visual traces and their visibility for each camera view (TRACE), while also predicting actions from the latent plan (ACT). At inference time, the trace prediction component is discard… view at source ↗

**Figure 2.** Figure 2: Architecture of SEETRACEACT. It receives camera views, a language instruction, a demonstration video, and robot states, and outputs an action chunk. We append learnable query tokens after the input tokens; their final hidden states form a visual latent plan, which is decoded into future end-effector traces during training. The trace decoder consists of a regression head that predicts the trace coordinates … view at source ↗

**Figure 3.** Figure 3: Cross-embodiment benchmark dataset in ROBOCASA-DC. For each of the 24 tasks, we pair 100 original Panda-arm trajectories with collected GR-1 humanoid demonstrations for training. For evaluation, we collect humanoid demonstrations for 50 pre-defined seeds per task. tasks. We collect each humanoid demonstration by restoring the corresponding Panda-arm trajectory’s initial state and teleoperating the humanoi… view at source ↗

**Figure 4.** Figure 4: (a) Four seen tasks and (b) four unseen tasks in the real-world benchmark. The yellow [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

**Figure 5.** Figure 5: Experimental results on the real-world benchmark with a Franka Panda arm. We report [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

**Figure 6.** Figure 6: Hardware setup for the real-world experiments. The setup consists of a Franka Panda arm, [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗

**Figure 7.** Figure 7: Target interaction regions in ROBOCASA-DC tasks. The highlighted region indicates the area in a static camera view where the robot must interact to complete the task. We use the area ratio of this region to the full camera view as the target interaction ratio (TIR) in §5 and [PITH_FULL_IMAGE:figures/full_fig_p017_7.png] view at source ↗

read the original abstract

Vision-language-action models (VLAs) are promising general-purpose robot policies, but adapting them to new tasks typically requires costly task-specific teleoperation data. As an alternative, we study one-shot demo-conditioned VLAs, where a robot policy is conditioned on a single demonstration video of an unseen task. We find that existing end-to-end approaches often struggle when successful execution requires precisely localizing small target regions. To address this limitation, we propose SeeTraceAct, a demo-conditioned VLA framework that encourages precise spatial grounding through visibility-aware prediction of future end-effector traces. To enable reproducible evaluation with cross-embodiment demonstrations, we introduce and release RoboCasa-DC, a demo-conditioned extension of RoboCasa with episode-paired humanoid videos. Experiments on RoboCasa-DC and a real-world benchmark, where a Franka Panda arm is conditioned on human demonstrations, show that SeeTraceAct outperforms baselines, achieving the best success rate across all four RoboCasa-DC settings and improving real-world average success by 12.5 percentage points.

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

SeeTraceAct adds visibility-aware end-effector trace prediction to demo-conditioned VLAs and reports gains on a new cross-embodiment benchmark, but the abstract leaves the implementation and ablations too thin to judge the source of the improvement.

read the letter

The core move is to condition a VLA on a single demo video and add a visibility-aware prediction of future end-effector traces so the policy can localize small targets more precisely. They also release RoboCasa-DC, which supplies paired humanoid videos and robot episodes for reproducible cross-embodiment testing.

The work does a clean job of naming a concrete failure mode in existing end-to-end demo-conditioned VLAs and showing that the added trace signal lifts performance. The real-world result (12.5 percentage point average gain on a Franka with human demos) and the consistent top rank across all four RoboCasa-DC settings are the strongest parts. Releasing the dataset is useful for anyone who wants to test one-shot adaptation without new teleoperation.

The soft spots sit in the missing details. The abstract gives no equations, architecture diagram, or training procedure for the trace head, so it is impossible to tell whether the visibility awareness is doing the heavy lifting or whether the gains come from extra capacity and tuning. The central assumption—that spatial grounding is the dominant bottleneck—receives no direct ablation in the summary, which leaves open the possibility that other factors (action chunking, embodiment alignment, or simply more compute) explain the numbers. Without those checks the soundness claim stays provisional.

This paper is for people working on imitation learning and VLAs who care about reducing task-specific data. A reader who needs a new benchmark or a concrete idea for spatial grounding would get value from the experiments and the released data. It deserves peer review because the empirical results are reported on relevant tasks and the dataset lowers the barrier for follow-up work, even if the methods section will need expansion.

Referee Report

0 major / 1 minor

Summary. The paper proposes SeeTraceAct, a demo-conditioned VLA framework that adds visibility-aware prediction of future end-effector traces to encourage precise spatial grounding when adapting to new tasks from a single cross-embodiment demonstration video. It introduces the RoboCasa-DC dataset (episode-paired humanoid videos) for reproducible evaluation and reports that SeeTraceAct achieves the highest success rate on all four RoboCasa-DC settings while improving real-world average success by 12.5 percentage points on a Franka Panda arm conditioned on human demos.

Significance. If the reported gains hold under full scrutiny of methods and ablations, the work would demonstrate a lightweight, additive mechanism for improving localization in one-shot VLA policies without requiring new task-specific teleoperation data, potentially easing deployment of generalist robot policies.

minor comments (1)

The abstract states that existing end-to-end approaches 'often struggle' with small target regions but provides no quantitative breakdown or example failure cases to support this diagnosis.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their summary of SeeTraceAct and for noting its potential significance as a lightweight addition to demo-conditioned VLAs. The report lists no specific major comments, so we provide no point-by-point responses below. We remain available to supply further details on the RoboCasa-DC dataset, trace-prediction ablations, or real-world Franka experiments if the editor or referee requests them.

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper introduces SeeTraceAct as an additive component to existing demo-conditioned VLAs, using visibility-aware future end-effector trace prediction to improve spatial grounding. No equations, parameter-fitting procedures, self-citations, or derivation chains are present in the abstract or summary that would reduce any claimed result to its own inputs by construction. The central claims are empirical performance gains on RoboCasa-DC and real-world benchmarks, which are not mathematical derivations and thus cannot exhibit the enumerated circularity patterns. The method is presented as an extension without load-bearing self-referential steps or uniqueness theorems.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Review is based solely on the abstract; no free parameters, axioms, or invented entities are described in sufficient detail to populate the ledger.

pith-pipeline@v0.9.1-grok · 5746 in / 1219 out tokens · 26385 ms · 2026-06-28T13:59:14.145878+00:00 · methodology

discussion (0)

Forward citations

Cited by 1 Pith paper

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

WatchAct: A Benchmark for Behavior-Grounded Robot Manipulation
cs.RO 2026-06 unverdicted novelty 6.0

WatchAct is a new benchmark of 3000 instances across 14 tasks in four cognitive domains for evaluating video-grounded robot manipulation, with current systems achieving at most 16.3% success.

Reference graph

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