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arxiv: 2109.13396 · v1 · submitted 2021-09-27 · 💻 cs.RO · cs.AI

Recognition: 2 theorem links

Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets

Bernadette Bucher, Chelsea Finn, Frederik Ebert, Georgios Georgakis, Karl Schmeckpeper, Kostas Daniilidis, Sergey Levine, Yanlai Yang

Pith reviewed 2026-05-13 19:51 UTC · model grok-4.3

classification 💻 cs.RO cs.AI
keywords robot learninggeneralizationcross-domain datamulti-task datasetdemonstration learningtransfer learningrobotic skills
0
0 comments X

The pith

A shared multi-task multi-domain robot dataset doubles success rates for new tasks in new environments when added to just 50 demonstrations.

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

The authors collect and release a dataset containing 7200 demonstrations of 71 tasks performed in 10 different environments. They test whether including this data during training helps a robot learn an entirely new task in an entirely new setting. When the shared dataset is combined with only 50 demonstrations of the new task, average success rates double compared with training on the target-domain data alone. Even a small number of demonstrations from the new domain suffice to let the robot perform many of its previously learned tasks there. The results indicate that reusable cross-domain collections can reduce the need to gather large task-specific datasets for each new robot project.

Core claim

By collecting a large multi-domain multi-task dataset with 7200 demonstrations of 71 tasks across 10 environments, the authors demonstrate that jointly training with this dataset plus 50 demonstrations of a never-before-seen task in a new domain leads to a 2x improvement in success rate compared to using target domain data alone. Data for only a few tasks in a new domain can bridge the domain gap and make it possible for a robot to perform a variety of prior tasks that were only seen in other domains.

What carries the argument

The Bridge Data collection, which supplies cross-task and cross-domain demonstrations so that end-to-end policies trained on it generalize to unseen tasks and environments.

If this is right

  • Robots can acquire new skills with far less per-project data collection.
  • A small amount of data from a new environment allows reuse of many previously learned skills in that environment.
  • Shared datasets become a practical way to bootstrap learning instead of starting from scratch each time.
  • Generalization improves without exhaustive data collection in every new setting.

Where Pith is reading between the lines

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

  • Growing the dataset with additional domains would likely further reduce the number of demonstrations needed for new tasks.
  • The same bridging approach could extend to different robot hardware or sensor suites.
  • If the dataset continues to expand, reliance on simulation for initial training may decrease.

Load-bearing premise

The collected tasks and domains are representative enough that cross-domain data produces positive transfer rather than interference for arbitrary new tasks and environments.

What would settle it

A new task and new domain in which adding the Bridge Data to the 50 target demonstrations lowers success rate below the level achieved with the 50 demonstrations alone.

read the original abstract

Robot learning holds the promise of learning policies that generalize broadly. However, such generalization requires sufficiently diverse datasets of the task of interest, which can be prohibitively expensive to collect. In other fields, such as computer vision, it is common to utilize shared, reusable datasets, such as ImageNet, to overcome this challenge, but this has proven difficult in robotics. In this paper, we ask: what would it take to enable practical data reuse in robotics for end-to-end skill learning? We hypothesize that the key is to use datasets with multiple tasks and multiple domains, such that a new user that wants to train their robot to perform a new task in a new domain can include this dataset in their training process and benefit from cross-task and cross-domain generalization. To evaluate this hypothesis, we collect a large multi-domain and multi-task dataset, with 7,200 demonstrations constituting 71 tasks across 10 environments, and empirically study how this data can improve the learning of new tasks in new environments. We find that jointly training with the proposed dataset and 50 demonstrations of a never-before-seen task in a new domain on average leads to a 2x improvement in success rate compared to using target domain data alone. We also find that data for only a few tasks in a new domain can bridge the domain gap and make it possible for a robot to perform a variety of prior tasks that were only seen in other domains. These results suggest that reusing diverse multi-task and multi-domain datasets, including our open-source dataset, may pave the way for broader robot generalization, eliminating the need to re-collect data for each new robot learning project.

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

2 major / 2 minor

Summary. The manuscript introduces Bridge Data, a multi-domain multi-task robotic dataset of 7,200 demonstrations spanning 71 tasks across 10 environments. Its central empirical claim is that jointly training on this dataset together with 50 demonstrations of a previously unseen task in a new domain produces an average 2x improvement in success rate relative to training on the 50 target-domain demonstrations alone; it further reports that limited data in a new domain can enable a robot to perform tasks previously observed only in other domains.

Significance. If the reported gains are robust, the work supplies concrete evidence that large-scale, reusable cross-domain datasets can materially reduce per-task data collection costs in robot learning, mirroring the role of ImageNet-style resources in vision. The open release of the dataset itself constitutes a reusable asset for the community.

major comments (2)
  1. [Experimental Evaluation] Experimental section: the manuscript reports an average 2x success-rate gain but supplies insufficient detail on training procedures, baseline implementations, number of independent runs per condition, observed variance, and whether statistical tests were used to establish significance of the improvement over the target-only baseline. These omissions make it difficult to rule out post-hoc selection effects or implementation differences.
  2. [§5] §5 (held-out evaluation): all reported test tasks are drawn from the same overall collection protocol and visual regimes as the training environments. This limits the strength of the claim that the dataset produces positive transfer for arbitrary new domains; the current results do not yet demonstrate robustness to substantial changes in lighting, object appearance, robot kinematics, or task structure outside the 10 environments.
minor comments (2)
  1. [Abstract] Abstract: the phrase 'on average leads to a 2x improvement' should be accompanied by the precise mean and a measure of spread (standard deviation or range) across the evaluated tasks.
  2. [Dataset Description] Dataset description: the selection criteria for the 10 environments and 71 tasks should be stated more explicitly so readers can assess how representative they are of typical manipulation scenarios.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and positive recommendation for minor revision. We address each major comment below and will revise the manuscript to improve experimental transparency and clarify the scope of our claims.

read point-by-point responses

  1. Referee: [Experimental Evaluation] Experimental section: the manuscript reports an average 2x success-rate gain but supplies insufficient detail on training procedures, baseline implementations, number of independent runs per condition, observed variance, and whether statistical tests were used to establish significance of the improvement over the target-only baseline. These omissions make it difficult to rule out post-hoc selection effects or implementation differences.

    Authors: We agree that additional experimental details are required for reproducibility and to strengthen confidence in the results. In the revised manuscript we will expand the experimental section to provide: a full description of training procedures including all hyperparameters, network architectures, and optimization settings; explicit implementation details for each baseline; the number of independent runs per condition (five runs were performed); observed variance reported as standard deviations; and results from statistical significance tests (paired t-tests) confirming the 2x improvement over the target-only baseline. These additions will directly address concerns about implementation differences and selection effects. revision: yes

  2. Referee: [§5] §5 (held-out evaluation): all reported test tasks are drawn from the same overall collection protocol and visual regimes as the training environments. This limits the strength of the claim that the dataset produces positive transfer for arbitrary new domains; the current results do not yet demonstrate robustness to substantial changes in lighting, object appearance, robot kinematics, or task structure outside the 10 environments.

    Authors: We acknowledge that the held-out tasks share the same overall collection protocol and visual regimes as the training environments. While the ten environments already include meaningful diversity in settings, objects, and lighting, the results do not demonstrate robustness to arbitrary new domains involving major shifts such as different robot kinematics or extreme lighting changes outside the collected data. In the revision we will update §5 and the discussion to more precisely scope our claims to positive transfer across the diversity present in Bridge Data, while explicitly noting this limitation for broader generalization. This clarification will better contextualize the empirical findings. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical success rates are measured outcomes, not reductions to fitted inputs

full rationale

The paper collects a multi-task multi-domain dataset of 7200 demonstrations and reports measured success rates on held-out tasks when training with the dataset plus 50 target demos. These results are direct experimental measurements rather than predictions derived from equations or parameters fitted inside the work. No self-definitional steps, fitted inputs renamed as predictions, or load-bearing self-citations appear in the derivation chain; the central claim rests on independent robot trials whose outcomes are not tautological with the data collection protocol.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the empirical observation that joint training on the collected data improves performance; it assumes standard imitation or reinforcement learning algorithms can leverage cross-domain demonstrations without negative transfer.

axioms (1)
  • domain assumption Standard policy learning algorithms can effectively utilize demonstrations from multiple tasks and domains without negative interference.
    Implicit in the joint training setup described in the abstract.

pith-pipeline@v0.9.0 · 5626 in / 1215 out tokens · 43714 ms · 2026-05-13T19:51:16.787575+00:00 · methodology

discussion (0)

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Forward citations

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