AutoBio: A Simulation and Benchmark for Robotic Automation in Digital Biology Laboratory
Authors: Zhiqian Lan
Yuxuan Jiang
Ruiqi Wang
Xuanbing Xie
Rongkui Zhang
Yicheng Zhu
LI PEIHANG
Tianshuo Yang
Tianxing Chen
Haoyu Gao
Xiaokang Yang
Xuelong Li
Hongyuan Zhang
Yao Mu
Ping Luo
Venue: ICLR 2026 Poster (2026)
Keywords: robotics
robot learning
vision language action model
biology experimental operation
AI for science
Primary area: datasets and benchmarks
TL;DR: AutoBio offers a novel simulation and benchmark with biologically-grounded manipulation tasks for precise, multimodal robotic automation in digital biology laboratories.

Abstract: Vision-language-action (VLA) models have shown promise as generalist robotic policies by jointly leveraging visual, linguistic, and proprioceptive modalities to generate action trajectories. While recent benchmarks have advanced VLA research in domestic tasks, professional science-oriented domains remain underexplored. We introduce AutoBio, a simulation framework and benchmark designed to evaluate robotic automation in biology laboratory environments—an application domain that combines structured protocols with demanding precision and multimodal interaction. AutoBio extends existing simulation capabilities through a pipeline for digitizing real-world laboratory instruments, specialized physics plugins for mechanisms ubiquitous in laboratory workflows, and a rendering stack that support dynamic instrument interfaces and transparent materials through physically based rendering. Our benchmark comprises biologically grounded tasks spanning three difficulty levels, enabling standardized evaluation of language-guided robotic manipulation in experimental protocols. We provide infrastructure for demonstration generation and seamless integration with VLA models. Baseline evaluations with SOTA VLA models reveal significant gaps in precision manipulation, visual reasoning, and instruction following in scientific workflows. By releasing AutoBio, we aim to catalyze research on generalist robotic systems for complex, high-precision, and multimodal professional environments.

Published as a conference paper at ICLR 2026
AUTOBIO: A SIMULATION AND BENCHMARK FOR ROBOTIC AUTOMATION IN DIGITAL BIOLOGY LABO- RATORY
Zhiqian Lan Yuxuan Jiang-.2*
Ruiqi Wang
Xuanbing Xie
Rongkui Zhang
Yicheng Zhu Peihang Li
Tianshuo Yang!
Tianxing Chen
Haoyu Gao
Xiaokang Yang' Xuelong Li
Hongyuan Zhang' Yao Mu Ping Luo 1.5t
'The University of Hong Kong
"China Telecom, TeleAI Tsinghua University
*Shanghai Jiaotong University
*HKU Shanghai Intelligent Computing Center
ABSTRACT
Vision-language-action (VLA) models have shown promise as generalist robotic policies by jointly leveraging visual, linguístic, and proprioceptive modalities to generate action trajectories. While recent benchmarks have advanced VLA re- search in domestic tasks, professional science-oriented domains remain under- explored. We introduce AutoBio, simulation framework and benchmark de- signed to evaluate robotic automation in biology laboratory environments an application domain that combines structured protocols with demanding preci- sion and multimodal interaction. AutoBio extends existing simulation capabili- ties through pipeline for digitizing real-world laboratory instruments, special- ized physics plugins for mechanisms ubiquitous in laboratory workflows, and a rendering stack that support dynamic instrument interfaces and transparent mate- rials through physically based rendering. Our benchmark comprises biologically grounded tasks spanning three difficulty levels, enabling standardized evaluation of language-guided robotic manipulation in experimental protocols. We provide infrastructure for demonstration generation and scamless integration with VLA models. Baseline evaluations with SOTA VLA models reveal significant gaps in precision manipulation, visual reasoning, and instruction following in scien- tific workflows. By releasing AutoBio, aim to catalyze research generalist robotic systems for complex, high-precision, and multimodal professional envi- ronments.
INTRODUCTION
Vision- lanooullaaage-ctioon (VLA) model architectures jointly leverage vision, language, and proprio- ception modalities generate action trajectories in an end-to-end manner. By aligning capabilities more closely with how humans perceive and interact with the world, these models hold promise as
pathway toward generalist robotic policies. Recent VLA models (Brohan al. [023] Kim al.)
2024| Ghosh et al. 2024: Liu et al., 2025 Black et al. [2024] 2025) have demonstrated impressive results in real-world scenarios, including table bussing, clothing folding, and household manipula- tion. However, current benchmarks remain largely confined to domestic settings, leaving a critical
gap in evaluating VLA models for professional, science-oriented scenarios.
Biology laboratories represent a uniquely promising yet challenging environment for robotic au- tomation. Experiments in these environments follow clear, rigorous protocols (Schilling et al., 2008), making them well-suited for language-guided robots to interpret and execute. Addition- ally, the repetitive and time-consuming nature of many laboratory tasks presents opportunities for automation to reduce researcher workload and enhance efficiency. Existing solutions, such as au- tomated sample preparation platforms (May 2016), often prioritize throughput at the expense of
"quual contribution.
"orresppnndigg authors. Email: pluo@es.hku.hk, muyan@sjtu.eduu.cm

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Bio experiments The "What"
Biological primitives The "Why"
Motion primitives The "How"
VLA capabilities The "Enabler"
Transfer
Combination
"Muce bishopioni sumoloe cusiniours sputiall"
"Combine moutetiale to form misturss supprnaioon
Reach & align
Precision control
Conditioning
Measurement
Seporotee components with a aominiaaee"
"Change store temperoture, miring) of somple"
"cunnttyyrsppryyy or state moterioly"
Relocate
Instrustion following
Separation
Preservation
"Spporat components usa citertal
"Pepare samples for long beree maiiteiiaae"
Actuate
Visual reasoning
"combiine trinnger sample soth a pipare"
AutoBio simulator
Robot systems
AutoBio benchmark
MujoCo
pperforna FCR
Physics
Rendering
Planning
Demos Evaluation
Figure 1: AutoBio framework. AutoBio decomposes complex experiments into fundamental bi- ological primitives. These are then implemented via robotic motion primitives within special- ized simulation environment. AutoBio simulator features instrument digitization pipeline, custom physics plugins for lab mechanisms. and rendering stack supporting dynamic interfaces and trans- parent materials. This enables creation of biologically grounded benchmark tasks to evaluate VLA models on precision control, instruction following, and visual reasoning capabilities in scientific workflows.
flexibility, whereas robotic systems could achieve human-level adaptability. Nevertheless, biologi- cal experiments impose distinct challenges on robotic intelligence: Long-horizon workflows require perception and interaction with diverse interfaces (c.g., digital displays, control panels, and articu- tasks like slot alignment are ubiquitous, while transparent
lated mechanisms). rrecision-depende liquids and containers complicate visual reasoning. These characteristics make biology laboratories an ideal benchmark for evaluating VLA capabilities in language grounding, visual understanding, and high-precision manipulation.
To realize this potential, however, it is critical to first address the challenges of simulating biol- ogy laboratories-at task that demands advanced asset modeling, physics simulation, and rendering capabilities. Robotic simulations typically use physics engines such as MuJoCo (Todorov et al. 2012). Bullet (Coumans & Bail 202T), and PhysX to provide a foundation for physics-based interaction. Building upon these engines, each simulator offers its own set of features tailored to specific focus areas. For instance, robosuite (Zhu et al.) (0200) and MuJoCo Playground (Zakka al. 2025) primarily target reinforcement learning (RL) (Sutton et al., 1998) algorithms through standardized tasks involving pick-and-place operations, articulated object manipulation, and loco- motion. Robo (Mu et al. 2025) emphasizes dual-arm coordination and proposes automated pipeline for data synthesis. These simulations generally address out-of-the- contact-based rigid body interactions, and lack specialized capabilities required for biological experimentation.
In the AutoBio simulator, we extend asset modeling, physics simulation, and rendering capabilities to streamline the entire pipeline of biological experiment simulation, specifically targeting the bio- logical primitives outlined in Figure [ In biological experiments, many operations rely on special- ized instruments such centrifuges, thermal cyclers, and mixers. We develop a workflow to trans- form real-world instruments into manipulable assets within AutoBio through 3D Gaussian Splatting (Kerbl et al.) (023), CAD refinement, and texture baking. These transformed instruments are then paired with self-contained logic to faithfully replicate their real-world characteristics. Regarding physics simulation, while the physical world operates under universal laws, physics engines typi- cally provide only generalized implementations, leaving finer-grained interactions to be extended by users. address this, we develop suite of MuJoCo plugins supporting interactions prevalent in biological experiments-inncudee and quasi-static liquid computation that are rarely addressed in existing simulators. Finally, ren- dering fidelity proves crucial as vision serves the primary input modality for VLA models. The prevalence transparent materials in laboratory environments poses particular challenges, as con- ventional blend-mode rasterization engines handle transparency poorly. We integrate our simulation
thread mechanisms, detent mechanisms, eccentric mechanisms,
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Table 1: Comparison of AutoBio with related robotic manipulation simulators benchmarks
Benchmark
Target domain
#Tasks
Interactive instrument
Threaded object
Floid
Reactive display
Render backend
Demo synthesis
model train & eval
(Simulator)
Meta-world
50
X
X
X
MuJoCo
x
Robosuite
9
X
X
X
Isaac Sim
X
x
Factory
Factory
8
V
X
X
Isaac Gym
X
X
Maniskill
20
X
SAPIEN
X
Robotwin
X
X
X
SAPIEN
Libero
Home
130
X
X
X
X
Isaac Sim
X
ChemistryãD
Chemistry
5
X
V
X
Isaac Sim
X
X
AutoBio (ours)
Biology
16
V
V
Blender
with Blender's Physically Based Rendering (PBR) pipeline to achieve visually accurate transparency effects for containers and liquids. In addition, implement dynamic texture rendering for instru- ment displays, enabling interactive UI manipulation-4 a critical feature for tasks involving digital interfaces.
By combining the above-mentioned simulation features, we propose the AutoBio benchmark, which distills robotic manipulation primitives into practical biological experiment tasks. We provide com- prehensive infrastructure to facilitate trajectory synthesis, demonstration generation, and standard- ized data interfaces for VLA model integration. Our benchmark comprises tasks across three diffi- culty levels, where we evaluate open-source SOTA VLA models, including To (Black et al., 2024), TT.5 (Black al., 2025) and RDT (Liu al. 2025). This evaluation reveals critical limitations in current approaches and suggests potential improvements in model architecture and training method- ologies.
Our key contributions summarize follows: (1) A simulator designed for biology labs, featuring instrument digitization, physics plugins (thread/detent mechanisms, quasi-static liquids), and PBR rendering for transparency and reactive displays. (2) A benchmark with biologically grounded tasks, enabling standardized evaluation of robotic automation in lab protocols, with support for trajectory synthesis and VLA integration. (3) Systematic evaluation of VLA models in science-oriented set- tings, revealing critical gaps in precision manipulation, instruction following and visual reasoning.
2 RELATED WORKS
Vision- Laaioon-anngag-Actttin Model. Recent generalist policies integrate vision, language, and con- trol for broad task generalization. RT-2 (Brohan al. (202) leverages web-scale vision-language data for robotic control, while OpenVLA (Kim et al. 2024) trained on 1M real-world de- mos-surpasses larger closed models various manipulation tasks. RDT (Liu al. 2025)
tends diffusion transformers to bimanual manipulation, enabling zero-shot generalization to novel objects and scenes. To (Black al., 2024) (and its successor To.5 (Black al. (025 combines VLMs with flow matching for smooth, cross cross-embodiment action generation. However, the bench- mark tasks typically involve coarse manipulation (picking, placing, folding) rather than precise lab procedures. LLong-horizar high-prec high-precision tasks and interacting with digital interfaces are rarely evaluated. AutoBio is intended to fill this gap by focusing on lab-specific objects and instruments, explicitly testing fine-grained lision-angguage-cctoo reasoning not covered by prior work.
Robotic Manipulation Benchmark.
Existing benchmarks like ManiSkill (Gu et al. 2023). ). Meta-
World Yuu et al., 2019), robosuite (Zhu et al. 2020), Libero (Liu et al. 2023), ) ARMBench (Mitash et al. 2023), and Factory (Narang et al. 2022) focus on rigid-object manipulation (e.g., pick/place, 2024b) introduces science-oriented tasks for chemical experiments, and BEHAVIOR-IK ( et al. 2024a) covers de-
assembly) in home, warehouse or factory settings. While Chemistry (Li et al.
formable settings, most lack support for fluids, digital interfaces, or lab-specific precision. AutoBio addresses this gap by simulating biological workflows with fluid handling, transparent materials, and interactive instrument UIs chaallengs absent in prior benchmarks. A feature comparison with related benchmarks can be found in Table
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"brief centrifugation"
'cnttiiigation for micro tubes"
'refrigerate centrifigation centrigiae
"temperature controlied mixing"
"mixing
"PCR reuction
refine
unwrap
annotate
bake
Video
3D GS
Raw mesh
Refined model
UV texture map
Digital asset
Figure 2:
Digitized instruments for fundamental biological experiment operations, with example taken from vortex mixer demonstrating the proposed workflow for digitizing real-world instruments.
Robotic Automation in Laboratory. A parallel research thread builds specialized "self-driving" labs for science. Szymanski et al. (Szymanski et al.) (023) describe A-L.ab that fully automates solid-state materials synthesis with fixed hardware pipelines tailored to inorganic chemistry. In their system, robotic arms dose powders, handle furnaces, and transfer samples between instruments while ML algorithms propose recipes. Similarly, other efforts have automated enzyme engineering or pharmaceutical screens (Holland & D Rapp et al. 2024 Tom et al. (024). However, these platforms typically prioritize throughput: each piece equipment is specialized for a specific protocol, and human oversight is still needed for setup or maintenance (Holland & Davies 2020). AutoBio targets the complementary goal of flexible, human-like manipulation in the lab. Instead of crafting one-purpose machines, AutoBio's framework is designed to move toward handling novel protocols end-to-end: reading digital instructions, applying proper tools, and adapting on the fly, that lie beyond the scope of existing lab automation work.
AUTOBIO SIMULATOR
AUTOBIO ASSETS
With reference real-world biomedical laboratory, AutoBio constructs dimensionally accurate digital models of common biological experimental assets and integrates them into a virtual envi- ronment. To support robotic manipulation, AutoBio meticulously preserves the physical interac- tion properties of these digital assets- assets-including collision characteristics and articulated relation- ships-ti ensure physical fidelity in simulation. All digital assets, along with their associated phys- ical and visual properties, are formally described using the MICF modeling language.
Table 2: Categories of AutoBio assets
Type
Function
Asset
Instrument
Perform experimental procedures
Centrifuge, Thermal Cycler, Mixer, Pipette, Centrifuge tube, Cryovial, Pipette tip.... Multiple-slot rack, Pipette rack, Tip box UR5e, Aloha, Robotiq gripper, DexHand
Container
Handle and store samples
Rack
Host containers and tools
Robot
Execute manipulation
Our assets are broadly categorized into four classes, as listed in Table To ensure the visual and geometrical fidelity of the instrument models, we propose workflow that incorporates 3D Gaussian Splatting (3DGS) (Kerbl et al.) 2023) for modeling laboratory assets. As illustrated in Figure 2 the process begins with multi-view video capture of real-world instruments. We then apply the PGSR algorithm (Chen et al. 2024) to reconstruct high-quality 3DGS assets. Coarse 3D meshes are subsequently extracted to achieve surface reconstruction. The mesh from 3DGS often contains redundant vertices and irregular topology due to the discrete nature of Gaussian splats. To optimize the mesh for physical simulation, refine it in CAD modeling software, producing smoothed,
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Section View
"Click" posibions
Oscillating platform
Surface norm
Tube
6
Liquid. surface
Spherical pendulum
Tube body
SDF collision
Spring
Helix SDF
Knob
OF
Offset axis
Central axis
Inertia force
Throw of eccentric
Gravity
(a) Thread mechanism
(b) Detent mechanism
(c) Eccentric mechanism
(d) Quasi- liquid
Figure 3: AutoBio physics plugins
watertight low-poly version while preserving critical geometric features with annotated articulations. The refined model is exported in the gITF format, and then is processed with our custom automated tools developed to accelerate the pipeline. First, the texture generation tool UV-unwraps the mesh and bakes vertex colors from the high-poly source mesh onto a UV texture map, with features such automatic seam-aware padding, lighting normalization, and masking for unmatched regions. Second, the gltf2mjef converter transforms the textured and joint-annotated gITF model into simulation-rese ready MJCF file for MuJoCo via a lightweight configuration interface. More information on AutoBio assets are presented in the Appendix, including the set of simulation-ready assets we constructed and the implementation details of the automated tools.
3.2 AUTOBIO PHYSICS
AutoBio employs MuJoCo as its physics engine to simulate rigid-body dynamics, including inter- actions among robots, labware and instruments. This framework supports accurate multi-articulated system modeling with rich contact, which are essential for replicating laboratory operations such as pipetting, tube handling, and instrument manipulation. In addition to MuloCo's native physics fea- tures, AutoBio incorporates customized plugins to efficiently simulate aaboratory-specinn physical behaviors. As depicted in Figure 3, these plugins include:
Thread mechanism models the assembly and mechanical properties of mating threads (e.g.. the cap-tube assemblies of centrifuge tubes). Inspired by the separation design of collision and visual geometry MuJoCo, we propose thread meshes in the collision detection, different and more efficient approach compared to prior works (Narang et al. 2022 Jiang al. 2024). An approximate method for computing this helix SDF is provided in the Appendix. As illustrated in Figure [3a the collision between the tube and the cap is induced by pair of coaxial helical threads with identical pitch. Leveraging MuJoCo's bDF-based collision solver, we are able to simulate screw motion between threaded objects as well the phenomenon of frictional self-locking (through appropriate friction coefficient settings). In contrast to mesh- basdd collision detection, the SDF approach is agnostic to the convexity of shapes and therefore avoids the need for convex decomposition of thread geometries. This substantially reduces the computational burden of collision handling while preserving high physical fidelity.
circular helix's 's signed distance function (SDF) to substitute
Detent mechanism simulates incremental motion with discrete "click" positions in lab instruments, such as stepped knobs or handles. Specifically, the detent mechanism provides tactile feedback via passive spring force generated through relative displacement with the nearest gear position.
Eccentric mechanism generates oscillating motion through off-axis rotation, enabling realistic sim- ulation of mixers (e.g., vortex mixer). The plugin achieves eccentric orbital motion through nega- tively coupled rotations about two parallel joint axes.
Quasi-static liquid provides an approximate yet efficient simulation of liquid shape deformation within containers, complementing MuJoCo's limitation in fluid modeling. Specifically, this module treats the liquid surface as a planar interface, neglecting wave propagation and pouring effects. This simplification allows to describe liquid deformation using only two states: the liquid level height and the surface normal vector. The motion of the surface normal is governed by damped spherical pendulum system in reponse to external container acceleration. We derive the ODE system
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Reactive display
PBR shader
Photorealistic
Scene
=4
OpenGL..
Lightweight
Conditioning buttons
(a) AutoBio rendering backends
(b) Reactive user interface
Figure 4: AutoBio rendering features
of the surface normal through analytical mechanics and Euler-Lagrange equation (see appendix for details). After the normal vector acquired, the liquid body is computed as the intersection of the oriented halfspace and container inner surface. We compute the surface height through volume conservation constraint, thereby generating liquid geometry within the container.
3.3 AUTOBIO RENDERING
AutoBio adopts a flexible rendering strategy, offering two rendering backends to accommodate both fast visualization and photorealistic rendering, summarized by Figure 4al Our simulation features are equally supported by two backends, but with different visual fidelity. Additionally, it implements real-time texture rendering for reactive panel interfaces in laboratory instruments.
Basic render directly utilizes MuJoCo's native OpenGL renderer, which offers fast but limited visualization. Rendering artifacts would occur when visualizing nested transparent objects, such as transparent tubes containing liquids, due to depth sorting errors. So, we fallback to mark some surfaces as opaque for more meaningful results.
Advanced render bridges MuJoCo simulation state to Blender's rendering pipeline, leveraging its physically based rendering (PBR) shaders to achieve photorealistic materials grounded in real-world light behavior. Particularly for container-associated manipulation tasks, this solution enables accu- rate rendering of transparent materials inceurails-nnuudinnn polyethylene, glass, and liquids liquids-through con- figurable optical parameters such as transmission coefficients, refractive indices, and surface rough- ness.
Reactive user interface employs dynamically loaded texture maps to render control panels and displays for some lab instruments, providing visual feedback for robotic manipulation, shown in Figure 4b This module maintains full compatibility with both rendering backends described previously.
4 AUTOBIO BENCHMARK
Building upon the capabilities of the AutoBio simulator, we develop the AutoBio benchmark, a suite of biologically grounded tasks designed to evaluate robotic automation in laboratory settings.
4.1 TASK GENERATION
AutoBio benchmark tasks are defined through unified procedure that specifies 1). randomized scene initialization; 2). procedural demonstration generation; and 3) task evaluation, with each step expanded below.
Randomized Scene Initialization. For each task, the environment initialized with randomized parameters to enhance diversity of generation. Randomization typically includes variations in the robot's initial joint angles and in the spatial placement task-relevant objects (e.g.. different initial and target positions of centrifuge tubes in the transfer task). Additional domain randomization is supported both in physics (c.g.. injected control noise) and visualization (e.g., color and lighting), with detailed task-specific settings provided in Appendix B.2.

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#2
(a) Close thermal cycler lid (easy)
Init.
#1
#2
Terma
(b) Unscrew centrifuge tube cap (medium)
Init.
#2
Term.
(c) Operate thermal mixer panel (hard)
Figure 5: Task progression across three difficulty levels. Each step includes a bordered inset (top- left) showing supplementary camera perspectives for contextual clarity.
Procedural Demonstration Generation. Expert demonstrations are generated via procedural poli- cies that decompose each task into sequential subtasks (e.g., reach-grast Barch-grasp-lin in the "Pick up cen- trifuge tube" task). For each subtask, we define an end-effector motion path conditioned on the subtask's initial state annotated object keypoints. The corresponding joint-space trajectories computed using inverse kinematics combined with time-optimal path parameterizaaion (TOPP), and the resulting motions are executed via PD control. Subtasks are then concatenated to form complete expert trajectory.
Task Evaluation. Task progress and success are determined through predefined status checks. These include monitoring contact events, object poses, and task-specific metrics. Trajectories that fail to meet these success criteria are discarded and not recorded for training.
4.2
TASK CATEGORY
To systematically evaluate VLA models, AutoBio consists of 16 tasks that are categorized into three difficulty levels (Figure 5). characterized by progressively increasing demands on precision, language understanding, and visual reasoning:
Level (Easy): These tasks feature low demands on vision and manipulation precision, exem- plified by straightforward actions like closing a thermal cycler lid (5a). Task requirements and language instructions are generally static, making them suitable for initial system integration and basic policy verification.
Level (Medium): Tasks this level, such unscrewing centrifuge tube cap 5b). present increased challenges in visual perception and manipulation precision. Language instructions may vary based randomized task parameters (e.g., target position), requiring models exhibit ba- sic generalization and instruction following capability beyond memorization. These tasks are designed evaluate fundamental VLA model performance.
Level 3 (Hard); High-demand tasks like operating thermal mixer panel (5c) require visual reasoning, fine-grained manipulation, and robust instruction interpretation. Successful completion often necessitates effective closed-loop control and the ability to perform complex cross-modal reasoning. These tasks are intended to rigorously challenge the capabilities of state-of-the tate-off-heear VLA models in scientifically relevant scenarios.

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Easy
Medium
Hard
No..5
0.8
RDT
DP
0.6
ACT
Score
044
0.0
Close themal cycler lid
Open thermal eycler lid
Pick up contsifugo tube
Unserew contrifuge tiee cap
Aspirate with pipette
Transfer contrifugo tube
Screw on centrifuge tube cap
Operate thermaal mixer panel
Load contrifugo roter
Figure 6: Evaluation scores of VLA and baseline imitation-learning methods on AutoBio tasks of increasing difficulty, trained on 100 demonstrations. Each bar represents the mean score over 100 evaluation episodes, averaged across three runs, with standard error of the mean shown as error bars. For VLA methods, dimmed stacked segment indicates the smaller-scale (trained on 20 demonstrations) data variant. The full numerical results are provided in Appendix Table 4 and
EXPERIMENTS
5.1 BASELINES AND EXPERIMENTAL SETTINGS
We choose 3 tasks from each difficulty level (Table evaluate VLA capabilities. For each task, we generate 100 demonstration trajectories at a frequency of 50 Hz formatted as a LeRobot (Cadene al. 2024) dataset. The total training set consists of over 792k frame of data, equivalent to 4.4 hours of continuous recording. All tasks except Operate thermal mixer panel are scored binarily (1 for success, for failure). For Operate thermal mixer panel, use a relative progress score to better reflect performance difference due to observed policy difficulties. Task details are provided in the appendix.
T0.5 BBlakk et al. 2025), To BBlakk et al. 2024) and RDT (Liu et al., 2025). We adapt our data each model's requirements (proprioception dimensions, im- age history, normalization), then finetune with default fine-tuning configurations starting with their pretrained checkpoints (bass- To- and RDT-1B). Adaptation details and model differences can be found in the appendix.
We evaluate three open-source VLAs:
To examine data scaling effects, we train each model on both full (100 episodes) and reduced (20 episodes) datasets while maintaining consistent normalization. Each configuration undergoes three seeded runs, trained for 30000 steps with batch size 32. A single run takes between to 14 hours on an NVIDIA H800 GPU, resulting in total runtime of approximately 2000 GPU hours.
In addition to the main VLA experiments, we conduct two supplementary experiments about imita- tion learning baselines and long-horizon task setting, with experimental settings and results detailed in subsection 5.3 and appendix B.5 respectively.
5.2 EXPERIMENTAL RESULTS
Policies are evaluated over 100 episodes per task, with aggregated scores presented in Figure The structured visualization highlights several consistent trends across difficulty levels and task families.
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Model-level comparison. Neither VLA demonstrates universal superiority, but distinct patterss emerge. RDT shows more stable performance on easy manipulation tasks, whereas To excels on medium and hard tasks requiring visio- -iii-linguittic grounding multi-step precision (e.g.. Operate thermal mixer panel). The more recent To.5 performs similarly to xn overall; its primary improve- ment is substantial boost Pick up centrifuge tube, pushing all easy tasks near 100% success. Notably, To.5 does not close the performance gap on medium/hard tasks, reinforeing that the failure modes we identify persist across generations.
These differences likely stem from architectural design: the PaliGemma-style joint-attention back- bone and fully trainable parameters of the r--eeiee allow richer cross- cross-modal adaptation than RDT's frozen pretrained encoders, which limits its flexibility in visually dense or instruction-heat tasks.
Effeet of data scaling. The m-series models benefit noticeably from additional demonstrations across most tasks, consistent with their ~3B trainable- trainable-parameterr capacity. In contrast, RDT displays minimal improvement with more data, reflecting the limited adaptability of its static encoders.
Task difficulty patterns. The empirical difficulty ordering aligns with our benchmark design. Easy-task failures primarily involve minor gripper slippage or end-effector drift. Medium and hard tasks expose deeper limitations in precision and consistency: tasks involving screwing/unscrewing motions accumulate compounding errors, suggesting that open-loop imitation is insufficient and may require reinforcement learning or feedhack-conditionee policies.
Failure modes and their causes. Qualitative inspection reveals interpretable failure modes: (1) Cross-modal grounding errors occur in Transfer centrifuge tube, where models choose incorrect slots, and in Operate thermal mixer panel, where textual instructions must be grounded to small UI elements. Low input resolution imposed by current VLA preprocessing pipelines further ob- scures numeric readouts, leading to inconsistent target selection and oscillatory learning curves. (2) reasonnge limitations arise Aspirate with pipette (liquid-level reasoning) and Load cen- trifuge rotor (symmetry maintenance). Memoryless architectures struggle when critical visual cues leave the camera view, motivating future work on temporally coherent or chain-of-thouge reasoning. (3) Lack of closed-loop recovery is evident in contact-rich tasks (e.g.. Screw/Unscrew cap), where policies often commit to open-loop trajectories and fail adjust upon minor deviations, indicating the need for adaptive feedback-drin driven control.
visual
Together, these quantitative and qualitative findings confirm that AutoBio exposes precision, ground- ing, and multi-step reasoning gaps that are not captured by existing domestic-task benchmarks.
5.3 IMITATION LEARNING BASELINES
To contextualize VLA performance. we evaluate two smaller imitation-learning baselines: Diffu- sion Policy (DP, 262M parameters Chi al. (2023) and Action Chunking Transformer (ACT, ~52M parameters Zhao et al. (2023), ). Since DP and ACT lack language inputs, task-specific pa- rameters extracted from the textual prompts (e.g., target slot indices or mixer settings) are provided as structured numeric observations.
Figure Appendix Sablesjsummarizz their results. Both DP and ACT reach competitive perfor- mance on several low- and medium-difficuly tasks, demonstrating that high-level language ground- ing not required for all settings. However, these baselines struggle to scale to the full diversity of AutoBio tasks. The primary obstacle is the heterogeneity of state spaces across tasks (c.g., [row, coll for Transfer centrifuge tube versus [rpm, temp, time] for Operate thermal mixer panel), which prevents training unified model.
In contrast, VLA models use language as a shared representation of task goals and environment states, enabling single policy generalize across radically different task structures. This unified interface suggests stronger potential for multi-task scalability and, ultimately, for executing multi- step laboratory protocols in long-horizon settings.

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6 SUMMARY
This paper introduce AutoBio, a simulation framework and benchmark designed to evaluate robotic automation in biology laboratory environments. By extending laboratory asset modeling, physics simulation, and rendering capabilities, the AutoBio simulator streamlines the biological experiment simulation inspired by practical operation primitives. Built upon this simulator, we propose the Au- toBio benchmark with manipulation tasks across three levels of difficulty, to systematically assess (VLA) models. The experimental results show critical
the capabilities of ssiion-laguage--aee limitations of current VLA models in precision manipulation, instruction following and visual rea- soning, and suggest potential improvements in model architecture and training methodologics.
ACKNOWLEDGEMENT
This paper is partially supported by the National Key R&D Program of China No. 20.20222D000000 and the General Research Fund of Hong Kong No. 1720825...
ETHICS STATEMENT
This research adheres the ICLR 2026 ethical guidelines and upholds the principles of responsible research. We ensure that no personally identifiable, sensitive, or harmful data were used. Our experiments did not involve any human subjects vulnerable groups. We have considered the potential societal impact of our methods, including the risk of misuse, and believe that these contributions primarily advance scientific understanding and do not pose foreseeable harm.
REPRODUCIBILITY STATEMENT
We follow the reproducibility guidelines in the ICLR 2026 author guidelines. We will open source code, configuration files, and scripts to reproduce our results, including dataset construction, model training. and evaluation, on platforms such GitHub and Huggingface as soon as possible.
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A AUTOBIO SIMULATION
A.1 AUTOBIO ASSETS OVERVIEW
Figure presents the collection of simulation-ready digital assets included in AutoBio.
Desiand
URSe km
Acha
Themall nyw
Centrilgge 941
Cremdage 5e30
Alcha
15mi certrlage
Picstte
30mi contrllge
Moxel pam
tube
mriri-dt tale rirk
1.5ml contriage
Pieete mvi
SCLON spp
Luk
Ocktto vart fayo
minui
delelluk
Pgotic
Figure 7: The overview of AutoBio's simulation-ready digital assets.
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A.2 IMPLEMENTATION DETAILS OF ASSETS DEVELOPING TOOLS
Texture generation tool. The meshes extracted from 3DGS describe color information with vertex color, which cannot be applied directly to the refined meshes. This tool handles this issue by transfer- ring vertex colors UV texture maps by ray casting, with features including seam-aware padding, lighting normalization (color correction), and masking of unmatched regions. This produces a tex- ture with reasonable out-of-box appearance, as well as allowing subsequent manual editing.
gltf2mjef converter is utility we developed to streamline the integration of CAD-designed assets into MuloCo. It automatically transforms voint-annotated gITF models exported from WolidWorks or Blender-into simulation-ready MJCF files through lightweight configuration interface. This eliminates the need for manual MJCF editing and significantly accelerates the process of preparing complex articulated models for physics simulation.
configuration file specifies global model metadata (c.g., name, scale, timestep options) as well as body hierarchies, joint definitions, and actuator properties. Each section corresponds to gITF node and may optionally include additional parameters such as damping, stiffness, or control ranges, thereby providing flexible mechanism for defining both kinematics and dynamies. A sample file in toml style is provided below:
meta
name = "vortex_ni
scale 76.25 0.0016
0.122 m
option = (timestep="O.000
[body]
Vortex mixer body [platform]
Rubber platform: 600-3200 RPM -> 80 rad/s
parent = "body"
[platform. joint]
type "erbital"
reference = "plat.form-joint""
offset [0, 0020 0] # in body frame
extra = (damping="0.011, armature="0.1")
actuator { kind="velocity", kv="1", ctrirange="O 400"
[switch]
Switch: on/off/touch (truncated)
[knob]
Knob:
speed control 0-10 (truncated)
Asset creation time analysis. We report the typical time required to build single simulation-ready asset with our proposed workflow. The process begins with approximately minutes of manual video capture, followed by about 40 minutes of automated 3DGS training and mesh extraction. Subsequent CAD refinement and joint annotation, performed by experienced users, generally take 30 to 60 minutes depending model complexity. The remaining downstream automated steps, including texture generation and MJCF conversion, require around 3 minutes.
A.3 THREAD MECHANISM
parameterized circular helix in three-dimensional space can be mathematically expressed as:
Ht: rı, p) = [x. y, 2] = {rr cost, r sin f, pt]
In practical application,,, a helix always bounded. For the above helix formulation, we denote these bounds 2ml < t. < 2m/,, where and h represent the starting and ending thread counts measured
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from the zero position. Within this framework, the approximate SDF becomes: to = atan2 Pz).
P= lop
[ 40
10
k:
2xP
2x
2x
(2k(2 + to)
I' S k: < Ht
SDF(P)=
min(dr(22l) dr π to)}
k < 1 1' <
min((dp(2Th) (2h'T (o)]
where dp(t) represents the Euclidean distance between point P and the corresponding position on the helix parameterized by t:
dp(t) = H (t; - Pll2-
This approximation remains valid when the helix angle sufficiently small.
In practical implementation, the plugin has been adapted to MuJoCo as an sdf extension. While al- ternative methods exist to approximate screw motion such parenting the nut the bolt within kinematic tree and adding coupled hinge joint and slide joint constrained by screw motion these approaches present significant limitations: (1) The self-locking mechanism does not naturally emerge from such couplings and proves challenging to implement artificially; (2) Modeling tran- sitional states between fully coupled and completely free bolt-nut pairs becomes problematie. By employing SDF to characterize these interactions in manner more faithful to real-world physics, we address both challenges gracefully.
A.4 DETENT MECHANISM
The detent mechanism is modeled using passive force function that depends exclusively the generalized position and velocity states intrinsic to any dynamic system. For knob with discrete positions, the force calculation follows:
f(q. Q) = -klq 9;) Adj
where the target position index is determined by:
argmin 4:1, i i= 0,
In practical implementation, this mechanism is adapted to MuJoCo as a passive force exten- sion.
A.5 ECCENTRIC MCHANISM
Planar eccentric motion can be mathematicalyy represented through a planar transformation matrix, which admits the following decomposition:
0 1 ssin 0 sin 9
0
cos 0
coss 0
cos
sin 0
cos 0
sin (-sin
cos
sin
sin 0 cos
0
0 0
1
0 0
0 0
where denotes the throw of the eccentric. The right-hand side matrices correspond to the motions of two negatively-coupled hinge joints. Instruments employing eccentric mechanisms, such as vor- tex mixers, typically operate at high RPMs. Under these conditions, coupling two joints through equality constraints proves more robust than alternative solutions.
A.6 QUASI-STATIC LIQUID
The surface normal dynamics are governed by damped spherical pendulum system. Following analytical mechanics conventions, we present the system's Lagrangian and generalized force as:
2 ml² (o² sin? + 62) + ml (gx sin cos & + 9y sin @ sin 9- cos 8),
L
Q 1 - -
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[o, 0] are the spherical coordinates of the normal vector and the generalized coordinates of the sys- tem. represents the characteristic length, and m is system mass (which ultimately cancels out in the final equations). The vector 9 19 9y gz]" captures tame-varying accelerations resulting from both gravity and inertial forces. AØ, Ao are configurable damping coefficients. Applying the Fuler-Lagraage equation:
d dr. dt djj dq; we derive the following system of ordinary differential equations:
dl =Q
d
dt o d
0 = 0, dt
d dt d 9 dt In practical implementation, to avoid numerical instabilities when the system approaches simple pendulum behavior, we modify the denominator in the di system initializes with states aligned to gravity direction and zero velocity. The normal direction components are computed as:
u sin O cos 0 ml (-9r: fxr sin Φ + By cos Φ) sin 0 >0
ml2 sin?
ml2v3 sin cos ml (gz cos @ cos + gy sin e cos 0 + gs sin 0) Agvi
ml2
d ó equation to ml max{sin" 6, c). The
I sin cos Φ,
y sin sin Φ
COS ạb.
Following surface normal determination each timestep, compute the liquid body as the in- tersection between the oriented halfspace and container's inner surface. This implementation involves computing triangle-plam relations for the manifold mesh. Surface height computation pro- ceeds through volume conservation constraints, employing the previous height as an initial guess before refining via Newton- Bisect search.
A.7 RENDERING
The MuJoCo renderer utilizes the legacy OpenGL exeedffnnttion pipeline for scene rendering, which offers limited customization capabilities. Notably, its reliance on fxee-function lighting creates visual inconsistencies between textured and untextured surfaces under specular and ambient lighting conditions. For this renderer, provide best-effort support, with certain scene elements (such as liquid representations) appearing in simplified forms to minimize visual artifacts.
We develop bridging mechanism translate MuJoCo's scene definition (MjModel) and simula- tion state (MjData) into Blender's environment. While conceptually similar to MuJoCo's ongoing USD (Universal Scene Description) exporter, our solution specifically accommodates our workflow by enabling: (1) Importation of tuned assets from Blender gallery files, and (2) Direct generation of fully configured Blender scenes capable rendering features beyond MuJoCo's representational capacity.
A.8 REACTIVE UI
Our user interfaces currently operate in retained mode, meaning only repaints when changes oc- cur. For the MuloCo renderer, UI updates in the 3D environment are achieved through calls to mir_upl oadTexture following repainting. In the Blender renderer, the UI passed to the Image Texture node either static images or video streams.
B EXPERIMENT DETAILS
B. ROBOT CONHIGRRTTION
The AutoBio benchmark currently supports two primary robotic configurations:
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Aloha: This arm is equipped with its native gripper. Characterized by smaller reach and lower payload capacity, it is primarily employed in tasks involving lighter objects, such as handling individual centrifuge tubes.
URSe: This arm offers greater reach and payload. We provide two end-effector options for the
UR5e: (a) URSe-Robotiq: The UR5e paired with Robotiq 2F-85 parallel gripper. This configu- ration is suited for tasks requiring interaction with larger instruments or a wider operational range. (b) URSe-DexHande
The UR5e equipped with a DexHand 021, a 19-DOF dexterous hand. This setup is intended for tasks demanding more dexterity, such precise pipette operation. Recog- nizing that current VLA models are often optimized for simpler, low-DOF end-effectors, offer a simplified control mode for the DexHand. In this mode, most of its DOFs are pre-configured and fixed, with only the metacarpophalangeal (MCP) joint of the thumb actuated collectively to mimic gripper-like open/close action.
B.2 BENCHMARK TASKS
In the experiment section, evaluated VLA capabilities across 9 AutoBio tasks. Below we describe details of each task, as well additional tasks not present in the main experiment.
Init.
#2
Term.
Close thermal cycler lid (easy, s): "close the lid of the thermal cycler" Close and lock the thermal cycler lid using URSe-Robotiq robot, testing trajectory following for articulated object manipulation. The joint angles of the robotic arms are perturbed to add randomness to the task. This randomization also applies to all tasks below.
Init.
Term.
Open thermal cycler lid (easy, ~17 "open the lid of the thermal cycler" Unlock and open the thermal cycler lid using Wi55-Robotig robot, testing trajectory following for articulated object manipulation.
Pick up centrifuge tube (easy, ~10s): "pick up the centrifuge tube on the rack" Pick up centrifuge tube from its rack using an Aloha robot, focusing basic visual positioning. The tube placed random rack slot to promote generalization. This also applies to all tasks below involving tubes and racks.
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Unscrew centrifuge tube cap (medium, ~)7 "dual-Aloke arms unscrewing centrifuge tube cap: one stabilizes tube while the other twists cap" Remove the centrifuge tube cap using two Aloha robots. This evaluates dual-arm coordination and manipulation precision.
Aspirate with pipette (medium, ~14 "dual-URSa pipetting: one arm lifts centrifuge tube, the other aligns pipette tip and aspirates liquid" Aspirate liquid from tube using URSe- Robotiq (holder) and WRRSe-DeHHand (pipette operator). This tests dual-arm coordination, pre- cision, and visual reasoning. The liquid volume in the tube is randomized test liquid level sensing capabilities.
Term
Transfer centrituge tube (medium, ~)ss): "pick up the centrifuge tube and move it to the other rack, row row]), column / (target_coll coll" Transfer tube specified rack slot using URSe-Robotiq robot, requiring precise manipulation, visual positioning, and language instruction following. The target rack slot is randomized to verify instruction following.
Init.
#1
#2
Term
Screw on centrifuge tube cap (hard, ~31 s): "dual-Aloha arms screwing centrifuge tube cap: one grips tube while the other twists cap" The inverse of unscrewing, but with stricter precision requirements for proper alignment.
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Operate thermal mixer panel (hard, ~16s): "Adjust thermal mixer parameters, with speed (set_rpnn rpm, lemperature set {set_temp) "C, and time set (set_time) seconds" " Set parameters (time, temperature, frequency) on a mixer panel using a URSe-Robotiq robot following language instruction and UI feedback, evaluating visual reasoning, language understanding, and high-precision manipulation. The parameters are randomized to test cross- modal reasoning.
Init.
#2
Term.
Load centrifuge rotor (hard, ~11 8): "Insert second centrifuge tube into the slot that is sym- metrically opposite to the currently placed tube" Load tube into the correct rotor slot while maintaining symmetry with the existing tube, testing advanced visual reasoning and precise po- sitioning. The target rotor slot are randomized by setting the currently placed tube to different slots, and the rotor angle are also randomized.
Init.
Close centrifuge lid (easy, ~55s): "Close the lid of the centrifuge" Close and lock the cen- trifuge lid using a URSe-Robotiq robot, testing multi-step trajectory following for articulated object manipulation.
ferm. Close large centrifuge lid (casy, ~15 "Close the lid of the centrifuge" Close and lock the centrifuge lid using a URSe-Rood GRSe-Robotig robot, testing multi-step trajectory following for articulated object manipulation.
Init.
#2
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Close mini centrifuge lid (easy, ~10s): "Close the lid of the centrifuge" Close the centrifuge lid using UR5e-Robotiq robot, testing trajectory following for articulated object manipulation.
Torm.
Vortex mix centrifuge tube (hard, ~60s): "dual-Aloha arms vortex mix the centrifuge tube the vortex mixer: holds the tube, the other operates the vortex mixer gear (gear)) Vortex mix a centrifuge tube with two Aloha robots. This experiment comprehensively tests dual coordination, articulated object handing, UI reading, and hog-horizon performance.
The evaluation time limits extended by approximately 50 % compared to demonstration horizon to accommodate imperfect policy execution. All tasks except Operate thermal mixer panel are scored binarily (1 for success, 0 for exceeding time limit) based pose and contact requirements at checkpoints and terminal states. For Operate thermal mixer panel, we implement a weighted relative progress score to better reflect performance difference due to observed policy difficulties:
3
[final; target, 0 }
Score Σ
initial, target, 0)
wy max
normalized to (0, 1).
B.3 BASELINES
We summarize key differences of our baselines (Fo, To.5 and RDT) in Table 3 where some of them (proprioception dimensions, image history, normalization) lead to difference in data adaptation.
Table 3: Key differences between mo and RDT baselines
To, Wo.5
RDT
Architecture
Decoder-only
Encoder-decoder
Vision backbone
SigLIP So400m/14 224
SigLIP So400m/14 384
Language backbone
Gemma 2B+300M
T5 vI.1 XXL
Trainable weights
Full
V & I. backbone frozen
Action sampling
Flow matching
Diffusion
Normalization
All
Gripper width only
State dimension
32
128
Action horizon
50
64
Image history
0 (None)
1
B.4 EVALUATION RESULT TABLES FOR THE MAIN BENCHMARK EXPERIMENT
The raw numeric evaluation results for VLA and baseline imitation learning methods are summa- rized in Table and Table respectively.
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Table 4: VIA evaluation score measured over 100 episodes AutoBio tasks with increasing level of difficulty. The score is reported in percentage by computing the value and the standard error of the mean over three runs.
Demo
20
100
20
100
20
100
Easy level
Close thermal cycler lid
Open thermal cycler lid
Pick up centrifuge tube
To.3
96.3 = 0.9
99.4 = 0.3
57.3 = 0.9
95.6 1.4
31.3 1.2
95.9 + 0.9 53.7 + 5.9
96.3 = 0.7
99.7 = 0.3
73.0 3.1
96.0 2.1
13.3 + 0.9
RDT
100.0 + 0.0 100.0 + 0.0
100.0 0.0
99.0 = 0.6
45.3 + 6.8 57.7 + 1.2
Medium level
Unserew centrifuge tube cap
Aspirate with pipette
Transfer centrifuge tube
Wo.5
1.0 + 0.6
11.7 1.1
0.7 0.3
18.3 1.0
0.3 0.3
37.3 11.3 40.7 + 5.4
7To RDT
1.3 + 1.3
115
4.3 0.3
42.7 = 1.8
2.0
2.0 + 1.5
2.7 1.2
0.0
0.3 - 0.3
0.3 + 0.3
2.0 + 1.2
Hard level
Screw centrifuge tube cap
Operate thermal mixer panel
Load centrifuge rotor
To.5
1.0 + 0.0
1.7 + 0.2
2.2 + 0.4
11.3 1.2
2.0 + 0.6
16.0 + 0.3 14.7 + 1.3
7o RDT
0.7 + 0.3
2.0 + 0.6
0.8 + 0.2
7.5 + 0.6
2.0 + 0.6
3.3 + 1.2
8.3 + 4.4
0.2 + 0.1
1.6 0.5
1.7 1.2
1.0 - 1.0
Table 5: Evaluation scores of baseline imitation learning methods measured over 100 episodes on AutoBio tasks with increasing level of difficulty.
Easy level
Medium level
Hard level
DP
100.0
16.3 #0.7
0.0
0.340.3 38.341.8
1.040.6 71.343.3
0.0 4.7=1.9
24.7+2.2 4.7.0.3 7.342.3
5.7+1.2
ACT
100.020.0
100.0.0.0
98.7
3.343.3
B.5 ADDITIONAL EXPERIMENT RESULTS FOR LONG-HORIZON TASK
Human operators naturally decompose complex experiments into atomic subtasks, and execute step by step-a capability we test by combining Close/Open thermal cycler lid episodes (200 total) to evaluate trajectory concatenation. We assess performance by executing one subtask to completion before switching prompts (Table
Table 6: Trajectory concatenation evaluation result
Close
Open
Close-Open
Open-Close
Mu.5
99.3 + 0.3
95.7 0.9
3.7 0.9
15.3 + 2.0
Wo
100.0 + 0.0
96.0 1.6 0.6
3.0 1.2
87.3 + 2.7
RDT
98.7 0.3
88.0 3.2
4.3 2.8
8.7 + 2.2
After training mixed data, the performance of RDT drops slightly on the two original tasks Close and Open, while no 's performance mostly remains the same. Regarding concatenated tasks, since the terminal robot pose of Open aligns well with Close's trajectory, ma could achieve reasonable transition in Open-Close, yet RDT fails to effectively interpolate in- between. Close-Open fails more frequently for both models, due to handle grip orientation differences in demonstration at transition location. This suggests current VLAs primarily memorize trajectories during fine-tuning, with limited ability generalize smoothly transition between related tasks.
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C LLM USAGE DISCLOSURE
In accordance with the ICLR 2026 policy on LLM disclosure, we acknowledge the use of LLM in the preparation of this paper. The model was used strictly as a tool aid and polish the writing. All scientific content is the original work of the authors.
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Reviews:
[{"review_id": "EkyMYUsEdL", "reviewer": "Reviewer_qBEk", "summary": "The paper proposes AutoBio, a simulation benchmark designed specifically for laboratory tasks and environments. The authors provide a laboratory equipment asset generation pipeline, relevant physics plugins, rendering which supports e.g. transparent materials, and a data generation pipeline. The authors also benchmark VLA and IL baselines on tasks of varying levels of difficulty.", "questions": "- Are benchmarks on simulation performance available (e.g. simulation speed) to gauge evaluation speed, and potentially the applicability of online learning methods?\n- The authors note the potential multitask capability of the VLA models. Did authors run multitask experiments on the VLA and IL models?\n- Are there future plans to provide additional tasks (e.g. longer horizon, more subtasks, etc), leveraging the same physics and rendering framework?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "- Useful task suite in an area of robotic manipulation with few realistic benchmarks\n- Careful consideration of physics, rendering, assets, etc in the context of biology tasks\n- VLA and IL baselines are relevant and highlight weaknesses in more complex tasks\n- The presentation is clear and contributions well-explained", "weaknesses": "- Seeing as the realistic assets, physics, and rendering are a central focus, validation on a real robot setup (even on the simpler tasks) would support claims of realism\n- The paper notes VLAs may perform well as multi-task agents in the discussion, however this setting is not evaluated", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "rMNnjeY8RH", "reviewer": "Reviewer_CmrM", "summary": "The authors propose a high-fidelity simulator targeting the biology lab automation domain for robotics. This domain has particular characteristics, like the preponderance of specialized machines (e.g. thermal mixers, centrifuges), clear / colored liquids, and transparent/translucent material (e.g. test tubes) which present difficulties for off-the-shelf robotic simulators like MuJoCo and its base OpenGL renderer. To solve this, they introduce a pipeline which digitizes real lab instruments through a 3D Gaussian Splat representation, adds  MuJoCo plugins for specialized physics (e.g. thread, detent, and eccentric mechanisms, liquid deformations) and more realistic rendering for transparent liquids through Blender physically-based rendering (PBR) and dynamically loading texture maps to make simulated machine control panels and displays dynamic.\n\nIn addition, given this simulator, the authors then develop a benchmark targeting 16 biologically-grounded tasks across three difficulty levels (easy, medium, hard) to systematically evaluate language-guided robotic manipulation in lab protocols. They then build infrastructure to generate 100 demonstration trajectories and then carefully evaluate state-of-the-art VLAs and smaller imitation learning baselines after fine-tuning them on the demonstrations.", "questions": "As you mention, low input resolutions exacerbate instruction following. Is this an issue with the task setup in the \"Operate thermal mixer panel\" which actually prevents the task from being fully executed?\n\nCan you expand on the physical realism of the \"aspirate with pipette\" task? Presumably some level of tactile or force feedback is necessary for working with pipettes in the real world in order to control the amount of liquid aspirated.\n\nIs there any value in providing instrument readings as an observation to the policies directly? Presumably some lab devices are IoT-enabled, and it could be a reasonable test of whether perception is fundamentally limiting policy performance or not.\n\nDo you expect increasing the number of demonstrations to significantly affect performance on the medium/hard tasks? Further investigation on data scaling effects (perhaps for a subset of the tasks) could be highly informative.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "Overall, the paper provides clear motivation for a focused effort on targeting simulation for the biology lab use-case, a significant area of both research and industrial importance and therefore has the potential for high impact.\n\nThe work is a high-quality, well-executed set of improvements targeting the precise difficulties of simulation in this domain. In particular, the improvements to base Mujoco (both physics and rendering) are extremely relevant to the domain. In particular, interacting with the control panels / displays of specialized machines is relatively rare in robotic simulation, and dynamically loading texture maps to achieve dynamic feedback is an example of a small but highly important infrastructural addition.\n\nThe primary originality of the work lies in the comprehensiveness and thoughtfulness of new techniques targeting the domain itself. While each technique (e.g. adding fluid modeling) is not extremely novel, the breadth of these which target domain-specific simulation issues demonstrates creativity.\n\nFinally, the careful set of benchmark tasks and baseline evaluation of VLAs / imitation learning policies is likely to be highly impactful, demonstrating the clear gap between current SOTA methods on this benchmark. The analysis of these results is thorough.", "weaknesses": "Especially given that most of the work is aimed at achieving stronger realism, the paper would be strongly improved by any real-world experiments demonstrating that the methods result in transfer onto real robotic hardware.\n\nA significant aspect of the domain is executing longer-term procedures, and much of the work is motivated by the long task horizons. However, the main text has minimal emphasis on this aspect in the experiments / tasks, and the long-horizon task in the appendix is simply concatenation of short trajectories.\n\nThe simulation is also focused on static manipulation, which may preclude true autonomous execution of multiple steps in more realistic lab environments given the lack of mobility.\n\nThe evaluation trained separate models for each task and evaluated tasks independently, while VLAs are motivated by cross-task transfer and language generalization; evaluation of a VLA trained and tested across multiple tasks could be informative.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "k88y611rWe", "reviewer": "Reviewer_yC5L", "summary": "This paper presents AutoBio, a robotics benchmark in simulation evaluating robotic agents' ability to perform lab tasks/experiments important for biology research. It is motivated by increasing AI-based automation of scientific research and the importance and difficulty of lab research. The key contributions are a biology lab simulation environment, including implementations of simulated phenomena that are crucial for lab biology research; a benchmark of biological tasks; and systematic evaluation of VLAs and simpler imitation learning baselines. \n\nThe paper discusses the simulation implementation: first, a process for digitizing assets, particularly lab equipment. Next, physics implementations to augment existing simulation capability: thread mechanism, detent mechanism, eccentric mechanism, and quasi-static liquid. For rendering, the benchmark uses basic rendering from MuJoCo, contributes advanced rendering blending MuJoCo's simulation state with blender's rendering pipeline, and contributes a reactive user interface. The benchmark itself includes randomized scene initialization, procedural demo generation, and task evaluation via predefined status checks on various pieces of state information. Tasks come in three difficulty levels, increasing in difficulty of vision and manipulation precision as well as task requirement/language instruction variation and abstraction/complexity. \n\nThe experiments are conducted by fine-tuning VLAs (pi_0, pi_0.5, and RDT) on either 100 or 20 demo trajectories. Non-VLA imitation learning baselines are trained from scratch on the data. Results show that RDT is more consistent on easy tasks, while pi_0 and pi_0.5 are better in challenging scenarios. No significant advantage is seen from pi_0.5 over pi_0 on medium- to hard-level tasks, which the paper ascribes to high-level complexity and reasoning requirements. The paper also presents failure analysis: failures on easy tasks are usually due to gripper slippage, whereas medium and hard tasks are due to compounding precision manipulation issues; the paper suggests that this indicates a need for algorithms with stronger closed-loop learning abilities, including RL. Failures on these tasks are also due to language understanding limitations, and partial observability. These are exacerbated by low input resolutions, a limitation of the benchmark. Imitation learning baselines are able to match performance on a single task when they are trained on that task alone, but not in the multi-task setting.", "questions": "- What is the difference between \"what\" and \"why\" in Fig 1? \n- Questions about grounding failure analysis, as listed in Weaknesses\n- Where do the procedural policies for demo generation come from?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "### Quality\n- Design makes sense and is well-explained.\n- Task difficulty clearly matters and is differentiating, which is key - I find this to be the hallmark of a good benchmark \n- Failure analysis is really promising (see weaknesses for thoughts on how to improve, but great addition) \n- Experiment suite is very good! I especially appreciate the holdout experiments, both because I feel it's important to fundamental ML science, and because I suspect this is a domain where there will be plenty of unseen info for a long time. \n- Simulation additions are clearly valuable and significant \n### Clarity\n- Incredibly well-written paper overall! Well-structured and very easy to follow. \n- Visual figures are really useful \n### Originality and significance \n- From what I understand, there are bio-lab benchmarks, but this one's design is unique and well-proven \n- Very important problem and this paper presents a benchmark that took considerable work in various aspects, so no concerns about significance.", "weaknesses": "### Quality\n- Some simulation features presented do already exist, though I suspect not with the fidelity/design choices needed here, meaning they do not take away from the contributions of this paper. However, they would be worth adding to RW: transparency and liquids are simulated in multiple simulators including OmniGibson (BEHAVIOR-1K, Li et al. 2022), and a threading mechanism is used in TRANSIC (Jiang et al., 2024). \n- Failure analysis needs more detail. Would benefit from examples of failure modes, to convince the reader that the explanations are correct \n- Rendering issues leading to low-quality visual observations is a failure of the benchmark, not the method, and should be attributed accordingly \n- Some analysis/stats on the quality of the simulation would be valuable, though I think it's less central than better exposure of the method results \n### Clarity \n- Results are presented in simple tables. This makes them very hard to follow, especially for a benchmark where all the statistics are important. It would really help to have charts. This should include direct comparison between the baselines and VLAs, comparison between similar tasks, and direct comparison between difficulty levels, as in my opinion this is the most salient comparison toward judging the quality of the benchmark\n- Overall, results section just needs more structure. The figures I talked about are a big part of it, but also a claims-driven structure - right now it's quite an info dump, which is hard to follow. \n\nThe reason for the 6 is mainly that I want the results to be presented better. Given that, I would recommend a higher score.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "tTQjsaDmfd", "reviewer": "Reviewer_knDW", "summary": "The paper introduces AutoBio, a simulation framework and benchmark aimed at evaluating vision-language-action (VLA) models for biology lab automation. Beyond standard rigid-body interaction, AutoBio adds (i) a digitization pipeline to turn real instruments into simulation-ready assets, (ii) MuJoCo physics plug-ins for lab-specific mechanisms, and (iii) a dual rendering stack (fast MuJoCo + Blender PBR bridge) with reactive instrument UIs. The benchmark contains 16 tasks across three difficulty levels; the experiments train/evaluate π0, π0.5, and RDT on 9 tasks, plus DP/ACT imitation baselines. Results: near-ceiling performance on “easy” tasks; sharp drops on medium/hard tasks involving precision screw motions, UI following, liquid-level reasoning, and rotor symmetry.", "questions": "1. The \"Operate thermal mixer panel\" task requires reading a reactive display. The analysis notes that models struggle, in part due to low-resolution inputs obscuring the numbers. Was the \"basic\" OpenGL renderer or the \"advanced\" photorealistic Blender renderer used to generate the visual data for training the VLA models?\n2. When training a single policy across all 9 (or 16) tasks, does performance degrade vs per-task finetuning?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Well-Motivated and Novel Domain: The paper successfully identifies a clear and important gap in current robotics research. Moving from generalist domestic tasks to specialized, high-precision professional domains like a biology lab is a logical and necessary next step for the field. The unique challenges of this domain (precision, transparency, complex tool-use, protocol-following) are well-articulated.\n2. New Simulation Features: The authors engineered new capabilities to meet the domain's demands, like an asset digitization pipeline, custom physics plugins.\n3. Rigorous Benchmark and Clear Results: The benchmark tasks are well-designed and thoughtfully scaffolded from Easy to Hard. The experimental results are stark and unambiguous: SOTA VLA models fail significantly as task complexity increases. This demonstrates that AutoBio is a challenging and valuable benchmark that is not \"solved\" and will be effective at driving future research.", "weaknesses": "1. Lack of full, long-horizon protocol tasks: The introduction emphasizes \"Long-horizon workflows\"  as a key challenge. However, the benchmark consists of 16 discrete, relatively short-horizon tasks. The \"long-horizon\" experiment in the appendix (B.4) is weak, merely concatenating two easy tasks (open/close lid). A true test would be a multi-stage protocol combining several different primitives (e.g., pick tube, unscrew, aspirate, transfer to a new tube, place in centrifuge, operate panel).\n2. Evaluation uses 9 of 16 tasks: the main results cover a subset (3 per level). It’s unclear whether conclusions generalize across all 16 tasks; several compelling tasks in Appendix B.2 (e.g., vortex mixing, multiple centrifuge variants) are left unreported. A fuller sweep or cross-task generalization metric would strengthen the case.\n3. Analysis of VLA failures: The analysis that imitation learning fails on high-precision tasks due to \"compounding errors\"  is correct but not particularly novel. The procedural nature of the demonstrations  (i.e., \"perfect\" kinematic paths) is a known confounding factor for IL, as models struggle to mimic this non-human data. The paper would be stronger if it discussed this limitation or compared performance with teleoperated human data.\n4. No sim-to-real validation: the work is framed as a stepping stone to lab automation, yet no real-robot evaluation is presented. Given the Blender PBR bridge, a small real-robot demo (even on easy tasks) would substantially improve credibility.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)