DexBioLab: Benchmarking Dexterous Manipulation with Dynamic Experimental Phenomena in Realistic Laboratories

Xuanye Wu; Yue Shi; Yongcan Yang; Yiqun Wang; Zhaoqi Wan; Dayu Xia; Yanming Shao; Jiezhang Cao

cs.ROOct 9, 2026

DexBioLab: Benchmarking Dexterous Manipulation with Dynamic Experimental Phenomena in Realistic Laboratories

Authors: Xuanye Wu, Yue Shi, Yongcan Yang, Yiqun Wang, Zhaoqi Wan, Dayu Xia, Yanming Shao, Jiezhang Cao, +3 more

Organizations: Shanghai AI Laboratory · Huazhong University of Science and Technology · University of Southern California · Shanghai Jiao Tong University

Abstract

Automating biological laboratories requires robots to perform dexterous tool interactions under realistic visual conditions while handling dynamic experimental phenomena, presenting significant challenges for robotic manipulation. To enable systematic evaluation under these conditions, we introduce DexBioLab, a simulation benchmark and framework for dexterous manipulation in realistic biological laboratory environments. DexBioLab comprises 14 tasks across 4 robot embodiments, spanning functional tool use, precision manipulation, language-conditioned visual grounding, and instrument operation. We introduce controllable dynamic experimental phenomena, including flames, vapor, and bubbles, to capture the evolving visual conditions of laboratory procedures. To further enhance environmental realism, we develop a hybrid rendering pipeline that integrates interactive simulated foreground objects with Gaussian-splatting backgrounds reconstructed from 6 real laboratory scenes. For efficient large-scale data generation and evaluation, we further build a parallel simulation framework on top of mjlab, proposing Parallel Finite-State Machine (PFSM) and integrating batched rendering with decoupled trajectory replay for demonstration generation and model-independent closed-loop policy evaluation. Our evaluation reveals substantial variation in policy performance across tasks, exposing distinct strengths and weaknesses of current manipulation policies. Real-world experiments further demonstrate a reduced sim-to-real gap, supporting the validity of AutoBio+ for realistic manipulation evaluation. Supplementary materials are available at https://cumulusalpha.github.io/DexBioLabWebPage.

Figures & tables

Benchmark	Biolab	Dexterous Manipulation	Realistic Rendering	Parallelization	Multi-Embodiment	Dynamic Phenomena
Bi-DexHands [ 2 ]	✗	✓	✗	✓	✗	✗
DexVerse [ 3 ]	✗	✓	✓	✓	✓	✗
DexJoCo [ 4 ]	✗	✓	✗	✗	✗	✗
Chemistry3D [ 5 ]	✓	✗	✓	✓	✗	✓
LabUtopia [ 6 ]	✓	✗	✓	✗	✗	✗
Labimus [ 7 ]	✓	✓	✓	✗	✗	✗

TABLE I: Comparison of DexBioLab with existing benchmarks. ✓ denotes full support, ✓ denotes partial support, and ✗ denotes no support.

Fig. 2: Tasks Overview and Dynamic Experimental Phenomena. The top figure illustrates all 14 tasks in DexBioLab along with the interactive assets, while the bottom figure presents the 3 types of dynamic experimental phenomena in DexBioLab, highlighting their visual evolution and temporal variations.

Fig. 3: Grasp Synthesis. First, large-scale grasp candidates are generated via optimization-based methods (e.g., Dexonomy and Lightning Grasp). Then, parallel simulation rollout is performed, and candidates are filtered based on feasibility constraints such as workspace limits and collisions. Finally, manual verification is conducted to ensure human-like plausibility. The validated grasps are stored in a grasp dataset. PFSM Policy: A PFSM operates over all environments in a batched manner, where each environment corresponds to an individual state instance. Transitions are triggered based on observed states, and planned trajectories are stored in a buffer that outputs batched actions when required.

Panel of Table 4 — TABLE II: Task-wise success rates (%) on the 12 representative DexBioLab tasks: mean $\pm$ SEM over 3 seeds, 100 rollouts per seed, 300 rollouts total.

Task	Gaussian Background	Visualization	Language Instruction
Attach Pipette Tip	Fume Hood		Attach the pipette tip with the Shadow hand.
Aspirate with Pipette	Fume Hood		Pick up the pipette and move it directly above the cryovial to perform the pipetting operation.
Transfer Tube (XHand)	Real Tube-Rack Scene		Pick up the centrifuge tube and move it to the other rack, row [row], col [column].
Manipulate Tweezers	Lab Bench		Pick up the tweezers, move them to the cryovial, grasp it with the tweezers, and place it into the fogging liquid nitrogen flask.
Heat & Stir Agar Solution	Lab Bench		Place the beaker on the heating plate, turn the knob on, then return the knob to its home position with the Wuji hand.
Close Centrifuge Lid	Centrifuge Station		Close the centrifuge lid with the Wuji hand.

TABLE III: Visualization and language instruction of the fourteen DexBioLab tasks.

Task	Gaussian Background	Language Instruction
Open Thermal Cycler Lid	Reagent Cabinet	Open the thermal cycler with the XHand.
Close Thermal Cycler Lid	Reagent Cabinet	Close the thermal cycler with the XHand.
Operate Mixer Panel	Heating and Mixing Station	Set the mixer to [time] seconds, [temperature] degrees Celsius, and [speed] RPM with the XHand.

TABLE III: Visualization and language instruction of the fourteen DexBioLab tasks (continued).

Task	Object / visual randomization	Robot reset ranges (rad.)
Attach Pipette Tip	Tip slot sampled from the $6\times 4$ tip-box grid.	UR5e: $[-0.20,0.20]$ .
Aspirate with Pipette	Cryovial slot sampled from the $5\times 12$ rack grid.	UR5e: $[-1.89,2.76]$ ; Shadow: $[-0.95,1.12]$ .
Transfer Tube	XHand: source tube slot sampled from the $5\times 2$ rack. Shadow: target row $\in\{0,\ldots,4\}$ and column $\in\{0,1\}$ sampled; tube pose follows the selected slot.	UR5e (XHand): $[-0.20,0.30]$ ; UR5e (Shadow): $[-1.89,2.07]$ ; Shadow: $[-0.95,1.12]$ .
Manipulate Tweezers	Tweezers $dx,dy\sim U(-0.025,0.025)$ m and yaw $\sim U(-0.10,0.10)$ rad; tube slot sampled.	Wuji: $[-0.12,0.12]$ .
Heat & Stir Agar Solution	Plate: $dx\sim U(-0.02,0.02)$ , $dy\sim U(0,0.02)\,\mathrm{m}$ ; beaker: $dx\sim U(-0.02,0.02)$ , $dy\sim U(-0.02,0)\,\mathrm{m}$ .	UR5e: $[-0.10,0.10]$ .
Close Centrifuge Lid	Centrifuge $dx,dy\sim U(-0.025,0.025)$ m.	UR5e: $[-0.10,0.10]$ .

TABLE IV: Task-specific randomization settings for the fourteen DexBioLab tasks.

Fig. 6: Raw training loss curves for 13 tasks. The orange panel denotes

\pi_{0.5}

training on real-world data for Transfer Tube (XHand), whereas the blue panels denote

\pi_{0.5}

training on simulated task data.

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Zhipeng Tang, Sihang Chen, Sha Zhang, Peihao Yang+10

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Robot Manipulation Benchmarks Dexterous Manipulation Robotic Manipulation Laboratory Automation Long-Horizon Robotic Manipulation

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Autonomous laboratories hold great promise for accelerating scientific discovery. To achieve this vision, robots are supposed to dexterously manipulate diverse labware and instruments and execute long-horizon, state-dependent experimental procedures. Yet existing benchmarks do not jointly capture dexterous hand use, real-world laboratory interactions, and multi-stage experimental procedures, limiting systematic training and evaluation. To bridge this gap, we introduce LabDex, a large-scale real-world dataset and benchmark for dexterous manipulation in chemistry laboratories, organized around a hierarchical task taxonomy spanning atomic skills, compositional tasks, and long-horizon experiments. First, LabDex is cross-platform and, for the first time, unifies real-world and simulation platforms under a common framework, providing standardized task definitions, demonstrations, and evaluation protocols. Second, LabDex is large-scale and systematically organizes chemistry laboratory operations into three interconnected levels: Atomic Skills, which characterize fundamental dexterous manipulation capabilities; Compositional Skills; and Long-Horizon Laboratory Workflows. This hierarchical design not only supports the evaluation of end-task performance, but also enables the analysis of how fundamental dexterous skills compose and influence more complex laboratory operations. We conduct cross-level evaluations of representative robot learning methods in both real-world and simulation environments. The experimental results validate the effectiveness of the LabDex task design and demonstration data, and show that the benchmark supports the training and systematic evaluation of existing robotic policies across laboratory dexterous manipulation tasks at different levels, providing a foundation for further research and development of autonomous laboratory robots.

Abstract

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