AstroBench: Benchmarking Free-Floating Capture and Space Robotic Manipulation

Zizhe Wang; Jia Deng; Xin Lu; Zehao Jia; Yi Huang; Yao Zhu

cs.ROOct 22, 2026

AstroBench: Benchmarking Free-Floating Capture and Space Robotic Manipulation

Authors: Zizhe Wang, Jia Deng, Xin Lu, Zehao Jia, Yi Huang, Yao Zhu

Organizations: Astroup Lab, Beijing, China · Harbin Institute of Technology, Beijing Research Institute

Abstract

Capturing a loose object inside a spacecraft requires a robot to approach a target that can translate and rotate, and whose motion changes when contact is made. These conditions motivate evaluation beyond manipulation of supported objects. We introduce AstroBench, an open benchmark for free-floating capture and space robotic manipulation with a fixed-base arm. The task suite combines four initial-motion tracks with sample-storage operations based on MELFI hardware and cap and captive-screw servicing based on the Robotic Refueling Mission. The two workcells provide nine task definitions and 29 task-target combinations. A metadata-filtered Objaverse inventory supplies 8,414 object instances for asset expansion, alongside five cabin visual contexts and versioned task instructions. We report two acquisition-focused experiments using existing checkpoints. A frozen $π_{0.5}$ DROID joint-position policy captures 2 of 200 static targets and 4 of 200 moving targets across twenty object identities. A cube-specific ACT policy captures 8 of 10 static targets and 6 of 10 moving targets in a separate experiment. Each experiment retains its recorded observation, action and capture configuration. The results document difficult direct transfer for the tested generalist checkpoint and successful task-specific capture behavior, while identifying concrete settings for studying motion-aware perception and contact-sensitive control. Code, task definitions and editable workcell assets are available at https://github.com/AstroUpLab/AstroBench.

Figures & tables

Fig. 1: AstroBench combines free-floating capture, MELFI sample-storage operations and RRM cap and fastener servicing. Panel (a) is a recorded $\pi_{0.5}$ rollout; panels (b) and (c) show the current workcell geometry. The capture target is unsupported; the storage and service hardware is secured to its worksite. The RRM panel is wall-mounted, with a floor-mounted arm centered in front of it.

Benchmark	Emphasis relevant to this work
LIBERO	Transfer across structured manipulation suites
CALVIN	Language-conditioned, long-horizon composition
RoboCasa	Diverse everyday tasks and visual environments
Space Robotics Bench	Mission-relevant robot learning in space environments
AstroBench	Free-floating capture and reference-based storage/service interfaces

TABLE I: Research emphasis of representative related benchmarks. This comparison describes the questions addressed rather than ranking platform performance.

Track	Linear speed (m/s)	Angular speed (rad/s)
Static	0	0
Drift	0.02	0
Tumble	0.02	0.5
Fast tumble	0.05	1.0

TABLE II: Initial-motion tracks in the released capture catalog. Magnitudes are shown; registered seeds determine the signed directions.

Workcell	Task	Targets	Initial condition	Goal evidence
MELFI	Card transfer	3	Card outside; gripper open	Card acquired and aligned with the selected opening
MELFI	Card insertion	3	Card held outside the box	Whole card inside; gripper released; storage restraint engaged
MELFI	Card extraction	3	Card inside the box	Card acquired and clear of the opening
MELFI	Lid opening	3	Lid closed and latched	Latch released and lid at the open angle
MELFI	Lid closing	3	Lid open	Lid closed and latched; tool clear
RRM	FDV1 removal and stowage	1	Cap seated and threaded	Cap removed with its adapter; assembly docked; driver withdrawn

TABLE III: Workcell tasks and their operation goals. The target count expands each task definition into concrete language-conditioned cases.

Setting	Frozen $\pi_{0.5}$ DROID	Cube-specific ACT
Training use	Existing checkpoint; no adaptation in this evaluation	400 local cube demonstrations; trained from random initialization; frozen at step 140,000
Objects	Twenty authored object identities	One Rubik’s cube
Initial states	One nominal pose per object, repeated ten times per condition	Ten fixed development positions, each evaluated once per condition
Conditions	Static / translation plus rotation	Static / translation plus rotation
Moving-target initialization	0.006 m/s along world $x$ ; 0.08 rad/s about world $z$	0.006 m/s along world $x$ ; 0.08 rad/s about world $z$
Physics / action rate	120 Hz / 15 Hz	120 Hz / 15 Hz

TABLE IV: Recorded capture configurations. Each column defines its own experiment; object coverage, initial states, detectors and action scheduling differ.

Condition	Attempts	Captures	Rate	Rejections
Static	200	2	1.0%	0
Dynamic	200	4	2.0%	2
Total	400	6	1.5%	2

TABLE V:

\pi_{0.5}

acquisition results. Rejections are included in the attempt denominator and shown separately.

Condition	Attempts	Captures	Rate
Static	10	8	80%
Moving	10	6	60%
Total	20	14	70%

TABLE VI: Cube-specific ACT acquisition results in the 20 s experiment.

Fig. 2: Acquisition measurements from the two recorded experiments. The $\pi_{0.5}$ panel resolves the twenty-object evaluation by object and motion condition; each cell has ten attempts. ACT is shown in a separate panel for its cube-specific experiment. Table IV specifies the settings of each panel.

Fig. 3: Recorded ACT capture of an initially stationary cube, shown at 0, 4, 8 and 10 s in one of the twenty evaluation attempts. The measured capture predicate first qualifies at 8.07 s. Frames illustrate approach and acquisition; the event time is obtained from the synchronized state trace rather than inferred from these sparsely sampled images.

Appendix figures & tables5 assets

Supplementary material from the paper’s appendix.

Appendix

Object	Static	Dynamic	S	D
	Captures / attempts		Rej.
Cube	0/10	0/10	0	0
Rectangular block	0/10	0/10	0	0
Cylinder	0/10	0/10	0	1
Sphere	0/10	0/10	0	0
Sample tube	1/10	0/10	0	0

Appendix

TABLE VII:

\pi_{0.5}

per-object capture counts. Each motion condition has ten attempts per object. Rej. counts predicted joint-limit rejections, included in the attempt denominators.

Label	Task ID	Exact English prompt template
M1	card_transfer_to_mouth	Move the card to the opening of the {target_name} and hold it aligned.
M2	card_insert	Insert the card fully into the {target_name}, release it, and leave it retained inside.
M3	card_extract	Remove the card from the {target_name} and hold it clear of the opening.
M4	lid_open	Open the lid of the {target_name} and leave it open.
M5	lid_close	Close and latch the lid of the {target_name}, then withdraw the gripper.
R1	fdv1_remove_stow	Unscrew and remove the {target_name}, retain it in its adapter, dock the adapter in its stowage holder, and withdraw the driver.

Appendix

TABLE VIII: Canonical workcell instruction templates. Resolve each target placeholder using the exact names in Table IX . The labels M1–R4 are local table abbreviations; task IDs are the machine-readable registry keys.

Templates	Target ID	Exact target_name
M1–M5	Front_quarter_box	front quarter box
M1–M5	Middle_quarter_box	middle quarter box
M1–M5	Rear_half_box	rear half box
R1	FDV1	FDV1 cap
R2	SMA_1_1	SMA cap at row 1, column 1
R2	SMA_1_2	SMA cap at row 1, column 2

Appendix

TABLE IX: Exact target IDs and instruction names. Each row is paired with every listed template, giving 29 distinct task–target combinations.

Fig. 4: MELFI storage hardware. (a) The mixed tray contains two quarter-size modules and one half-size module, with separate lid and tray locks. (b) An open quarter box exposes the hinge, curved perforated shell and released card-restraint band. (c) The sample card carries four vials below an unobstructed grasp region. (d) The half-box extraction reset places the card inside with the band routed around it. Panels (b) and (d) show task reset states. The historical mixed-box arrangement follows ESA and NASA documentation; the card’s $100\times 110$ mm planar dimensions follow ESA’s description [ 11 , 12 ] .

Fig. 5: RRM panel and tool interfaces in the accepted wall-mounted layout. (a) The full board locates the FDV1 interface, SMA array, captive fasteners and the two receivers below the panel. (b) The FDV1 cap and Tertiary Cap Adapter are shown in the centered ready configuration. (c) The $3\times 3$ SMA array supplies addressed cap targets. (d) The two captive-screw positions expose their drive interfaces. (e) The larger TCA receiver and smaller SMA-adapter receiver contain separate latch components. The views use the same final scene and part poses; the arm and waiting tool are hidden where they obstruct the hardware. NASA sources establish the reference panel and tool roles [ 14 , 13 , 15 ] .

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Contact-Rich Robotic Manipulation Robot Manipulation Benchmarks Generalization in Robotic Manipulation Tactile Sensing for Robotic Manipulation

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Contact-rich manipulation underlies many everyday human activities, yet evaluating robots on such tasks remains difficult: the tasks involve diverse contact behaviors and are hard to reproduce across laboratories. Drawing on observations of everyday human manipulation, we identify eight essential skills that provide a practical framework for studying contact-rich manipulation. We introduce ContactBench 1.0, an extensible benchmark that instantiates these skills through 36 reference tasks and more than 40 object assets. The assets capture task-relevant geometry and contact behavior, can be fabricated reproducibly on consumer-grade 3D printers, and are each paired with a matching simulation model. Controlled task variations in both the real world and simulation enable evaluation of how well robot systems generalize. We evaluate recent representative vision-only and visual-tactile policy learning methods on six benchmark tasks. The results reveal that while vision-only policies struggle to perceive or react to contact events, visual-tactile policies do not meaningfully improve their performance, highlighting that contact-rich manipulation remains a significant challenge for current policy learning methods. By providing common task definitions alongside physical and simulated assets, ContactBench 1.0 offers a shared testbed for developing, evaluating, and comparing contact-rich manipulation systems. The benchmark will be open-sourced at https://jingyi-xiang.github.io/contactbench.

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