House Of Dextra : Cross-Embodied Co-Design for Dexterous Hands
Authors: Kehlani Fay
Darin Anthony Djapri
Anya Zorin
James Clinton
Ali El Lahib
Hao Su
Michael T. Tolley
Sha Yi
Xiaolong Wang
Venue: ICLR 2026 Poster (2026)
Keywords: Co-Design
Manipulation
Robotics
Cross Embodiment
Robot Hands
Robot Learning
Reinforcement Learning
Hardware Design
Primary area: reinforcement learning
TL;DR: A fast method for sim-to-real co-design of robot hands for dexterous manipulation through cross embodied policy training and physicially realistic grammars

Abstract: Dexterous manipulation is limited by both control and design, without consensus as to what makes manipulators best for performing dexterous tasks. This raises a fundamental challenge: how should we design and control robot manipulators that are optimized for dexterity? We present a co-design framework that learns task-specific hand morphology and complementary dexterous control policies. The framework supports 1) an expansive morphology search space including joint, finger, and palm generation, 2) scalable evaluation across the wide design space via morphology-conditioned cross-embodied control, and 3) real-world fabrication with accessible components. We evaluate the approach across multiple dexterous tasks, including in-hand rotation with simulation and real deployment. Our framework enables an end-to-end pipeline that can design, train, fabricate, and deploy a new robotic hand in under 24 hours. The full framework is open-sourced and available on our website.

Published as a conference paper at ICLR 2026
House of Dextra: CROSS-EMBODIED CO-DESIGN FOR DEXTEROUS HANDS
Kehlani Fay!
Darin Djapri'
Anya Zorin'
James Clinton?
Ali El Lahib
Hao Su'
Michael T. Tolley
Sha Yi
Xiaolong Wang!
University California, San Diego
2 University of California, Santa Barbara
Embodiment Generation
Zero Shot Embodiment Build and Deploy
Cross-Embodiment Control
Rlpping
Graping
Figure 1: House of Dextra is cross-embodied co-design framework for dexterous hands which jointly optimizes control and hardware design. Using this framework, we generate four co-designed dexterous robot hands in simulation and transfer them zero-shot the real world.
ABSTRACT
Dexterous manipulation is limited by both control and design, without consensus as to what makes manipulators best for performing dexterous tasks. This raises a fundamental challenge: how should we design and control robot manipulators that are optimized for dexterity? We present a co-design framework that learns task- specific hand morphology and complementary dexterous control policies. The framework supports 1) an expansive morphology search space including joint, fin- ger, and palm generation, 2) scalable evaluation across the wide design space via morphology-condiite
cross-embodied control, and 3) real-world fabrication with accessible components. We evaluate the approach across multiple dexterous tasks, including in-hand rotation with simulation and real deployment. Our frame- work enables an end-to-end pipeline that can design, train, fabricate, and deploy a new robotic hand in under 24 hours. The full framework is open-sourced and available on our website
INTRODUCTION
Endowing robots with human-level dexterity critically requires both the development fine con-
trol (Wang al. 2025 Qi et al. 2023) Zeng al. 2019) and dexterous hardware design (Zorin

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Published as a conference paper at ICLR 2026
et al. 2025 Shaw et al. 2023 Unitree Robotics Allegro Hand Christoph et al. 2025). Tradition- ally, robot manipulation has been developed by decoupling mechanical design from control, with reinforcement learning and control frameworks developed on fixed hardware architectures. This separation can limit dexterity as optimal control policies may be fundamentally constrained by the morphology of the hand, degrees of freedom, and sensing capabilities (Yin al.) [025 Calandra et al. 2018). Co-design aims to overcome this limitation by simultancously adapting design and
control (Cheney et al. 2013 Pathak et al., 2019
Bai et al.
2025).
Additionally, previous works Yuaa et al. 2021: Lu et al.
2025
Carlone & Pinciroli 2019 Wang
al. 2023a Cheney al., 2013) have often been deployed only simulation. The sim-to-real gap remans for many works due to over simplifications in hardware manufacturing constraints Aljalbbut
et al. (2255, simplifications in simulated contacts to real world dynamies Huang et al. (2023) and modeling of physical properties[Zhan (Zhao et al. (2020b). To overcome this, we create a modular hardware platform with realistic components simulation, enabling designs for sim-to-real.
House of Dextra creates cross- cross-embodied co-design framework enabling quick and efficient eval- uation of co-designed robot hands and allowing design to sim-to-real in under 24 hours. Modular Robot Hand: We create robot hand platform with modular mechanical and electrical structure. This allows exploration of variable kinematics, degrees of freedom, and morphology. We show this by deploying four different robot hand embodiments sim-to-real. Accurate Robot Hand Genera- tion: We create modular and physically realistic grammars and rule structures that generate robot hands that can be built and deployed sim-to-real. Design Analysis: We expand co-design beyond primitive parameters for manipulation and identify parameters of impact for future dexterity re- search.
The result is a cross-embodied co-design framework with efficient evaluation and generation of robot hands. We demonstrate this approach sim-to-real for 18 novel objects with in-hand rotation across multiple generated designs. Results can be found on our website: HouseOfDextra
2 PROBLEM SETTING AND NOTATIONS
We jointly optimize robot embodiment and control so that structure and behavior are aligned. Let G denote the morphology space, II the policy space, and D S 9 the set of evaluated designs. Our goal is to co-design a hand morphology G G and policy
IT that maximize task reward.
Morphology Representation. In our implementation, each hand is represented as a fixed-topology attributed graph G = (V, E, Xz), with one palm node and five finger-slot nodes (VV 6). Edges encode only connectivity,
= {(0,))) i i.e., a star graph from palm to fingers. Each finger node has attributes v, E X.. including servo count, grammar codes, fingertip type, terminal/active flags, and finger index (with group context).
Graph Neural Networks. To account for isomorphic graph structure, encode each morphology graph G with a peessage-passing GNN fo- Given (V, X.), fo performs K rounds of message passing to produce fixed-dimensional embedding, v(G = (G) E IRd. This embedding used by the design value network during graph heuristic search evaluate and rank candidate morpholo- gies.
Control Policy. We model dexterous manipulation for fixed morphol- ogy G as an MDP Ma = AS,,, T, R, 7), where the dynamics depend on the embodiment. The pol- icy is conditioned where Fiax 5 and Lmax 3. The state is defined as (g. Po: m())). where q E R are robot joint states and po E R is the object pose.
Corphology-Condittonn
one-hot morphology vector m(G) (0, 1)dm with d,, Fiaax (1 + 2m
The action space A consists of joint position commands. Since not all actuators are present in all morphologies, we apply an action mask MGG E (0,1) to the raw policy output: as = (T(s)) @ MG), where denotes clement-wise multiplication. This allows shared policy to operate across different embodiments without producing invalid actions. The reward R(s,, a) is task-specific and depends object state, joint effort, object proximity the hand, and related task
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Cross Embodied Policy Training
Co-Design Generation
Sim-to- to-Real Generalization
Generated Random Embodiments
Design Generation
Policy Training
Design Encoder
s,a
Policy Network
Action Masking
Evaluation
Tπ(a s, g)
Figure 2: Method overview. Stage 1: We randomly sample embodiments across morphologies and degrees of freedom and pre-train Designs are generated using modular grammars and evaluated simulation by the cross-embodied policy. Stage 3: The best designs and fine-tuned control manufactured and deployed real hardware across unseen objects.
policy across embodiments. Stage 2:
terms. For fixed morphology G, the policy objective is
( G) = Erra Ma Σ
R(s an)
(I)
t=
where T = (so, ag .1,...) denotes trajectory in Mc.
Co-Design Bi-Level Optimization. The co-design problem jointly optimizes morphology and control:
(G* x²) arg GEG, æEnl
J(T, G).
Equivalently, this can written as the bi-level problem
G arg max J G): GEg
(3)
= = arg max J(T, G). xell
(4)
The inner loop optimizes control for fixed morphology, while the outer loop searches for the morphology with the best achievable task performance. Because this joint space is large, first train a cross-embodiment base policy it over sampled designs G ~ p(G), then use to efficiently evaluate and fine-tune generated designs during co-design.
METHOD
Our approach enables sim-to-real robot hand co-design through cross-embodiment learning across embodiment families. An overview of our method is shown Figure 2 We first generate set of diverse embodiments by randomly sampling design rules. Rather than learning separate policies for each hand design, we group morphologies into families based on shared kinematic structures, asym- metric symmetric, and variable finger counts. This enables efficient generalization across fingertip
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Figure 3: Left to Right. 1) Variable finger length of 1-10 using flat modular stacks. 2) Example
embodiment with varying degrees of freedom with fingers of or servo motors. 3) Fingertip Variations 4) Finger number and example embodiment variations.
types, degrees of freedom, link lengths, palm geometries, and finger placements within each family. To make the control policy aware of the morphology, while considering the isomorphism of design, we condition the control policy on encoded morphology. Our physical grammar system reflects real hardware constraints through pre-computed collision geometries, joint limits, and manufacturable component specifications, ensuring designs transfer effectively to physical systems.
Components. The framework consists of four main components:
Generation
~ Pgrammar (r, Procedural constraint hand generation (5)
Design encoder
= (G) Graph embed for design-value optimization (6)
Design value network
Vdesign (v()) b Learned design eval for search guidance (7)
Cross-embodiment policy
(a m(G) & One-hot morphology conditioned control
(8)
Here, y(G) = fo (G') is the graph embedding for design evaluation, m(G) E {0, 1)dm fm is the one-hot morphology vector for policy conditioning, and s, a denote state and action variables. The gram- mar generator encodes palm geometry, finger count ny
{3, 4, 4,5), per-finger joint configurations je E (2,3), segment-scale grammar codes 61, 62 E {l..... 10), and fingertip types using real mod- ular sub-components with realistic collision geometries and manufacturing constraints (Appendix Fig. 2). During co-design, the system iteratively generates candidate morphologies, evaluates them with the pre-trained cross-embodiment policy, and selects designs for manufacturing.
3.1 MODEL OBJECTIVE
Design evaluation leverages cross-embodiment to assess morphology performance. The design
value network learns from observed perfomnperfo pairs:
Edssign -Edr
where d indexes an evaluated design instance and T(d) represents the maximum observed task per- formance for that design under the cross-embodiment policy. The search explores through Graph Heuristic Search (Zhao et al. 2020a), maintaining a lookup table T D - IR that maps evaluated designs empirical scores and guides value network training. At each iteration, signs are generated through sequential grammar expansion. For each partial design, legal next rule applications are scored by the design value network, where v; the predicted value of the ith expan- sion. These scores are perturbed with Gumbel noise, of = + Tgi, where 9i Gumbel(0, 1) and is a noise scale controlling exploration strength. The next expansion selected by maximizing v/- This keeps the search value-guided while still exploring alternative design choices. The lookup table is then updated via T(d) max(T(d), J (Ga)) for newly evaluated designs.
candidate de-
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3.2 ARCHITECTURE AND TRAINING
Hand Generation for Cross-Embodiment Pre-training. We develop a parametric hand genera- tor that constructs diverse, physically-valid morphologies through two-stage process. First, the generator samples palm layouts by placing finger bases on variable circular radius under config- urable placement modes (symmetric, asymmetric, or anthropomorphie) discretizing the circle into slots with minimum angular separation enforced via rejection sampling. A convex palm mesh is constructed by computing a 2D convex hull of grip points around each motor position and extrud- ing it to a specified thickness. Second, each finger's internal morphology is independently sampled from a structured design space controlling: (I) the number of actuated joints (2-3 servos beyond the base), (2) two discrete grammar codes C12 C2 E {l,..., 10} that determine proximal and distal seg- ment scales, and (3) fingertip geometry (standard, wedged, rounded, or thin variants). This produces thousands of hand variants by sampling from uniform distributions across grammar parameters with fully randomized designs. Each generated hand includes pre-computed collision geometries via CoACD convex decomposition for all modular components, realistic joint limits derived from CAD specifications, and consistent kinematic representations.
Cross-Embodines Policy Pre-training. We group generated hands into kinematic families based on structural mnmilartty-Symmetrie radial configurations, anthropomorphic arrangements, and vari- able finger counts-enabling targeted policy learning within each family. The cross-embodiment policy uses PPO enhanced with conoollooyocooiiinnnnng.
min To(als, (iaalss,(G)) A., clip (ota(als, mG))
no(als, 8, m (G) (ald m(),,,m()))))))))) mGG) -, - c,1+e) A, (10)
].
Cpoticy (.)
([))
where mG) represents the one-hot morphology encoding provided to the policy. Each family trains separately on 2000-8000 hand variants, with the policy learning unified control strategies that gen- eralize across finger counts, joint configurations, and geometric variations within the family. This morphology conditioning enables the policy adapt its control strategy based on the specific kine- matic structure and available degrees of freedom.
Graph Heuristic Search Algorithm. Following cross-embodiment pre-training, we employ Graph Heuristic Search to optimize hand designs for specific tasks. At each iteration, the search gener- ates K candidate morphologies, evaluates them in parallel using the pre-trained cross-embodimenn policy, and updates both the lookup table T 'D - R and the design value network Vdesign based on observed task performance. The lookup table stores the best observed score for each eval- uated design and provides supervision for value network training. This allows the search to focus computation on promising regions of the morphological space while maintaining diversity through crammar-based design generation.
Design Optimization The search begins from an initial design with a randomly configured palm layout and randomized finger bases, marking fingers as non-terminal nodes requiring expansion. At expansion: for
each iteration, the algorithm generates candidates through sequential finger-by-fingg each non-terminal finger, it evaluates all valid parameter choices by constructing successor designs and scoring them with the GNN, which predicts performance from graph representations augmented with Gumbel noise (noise_scale x 0.4) for exploration. The highest-scoring successor is selected, repeating until all fingers are finalized into complete designs. These candidates are evaluated in par- allel simulation across multiple hand groups, yielding task-specific performance scores. The lookup table is updated with each complete design's score and its partial ancestors' scores (enabling credit assignment during construction). using effective keys that account for morphological equivalences (finger permutations in symmetric layouts, thumb slot variations in anthropomorphic layouts). The GNN is trained via supervised regression on this growing dataset, minimizing squared error between predicted and observed scores with L2 regularization and gradient clipping. This updated value net- work guides subsequent candidate generation with refined predictions, while an annealing epsilon schedule (0.4 - 0.05) shifts from exploration to exploitation, and leth-level tabu tracking prevents revisiting identical construction sequences within each batch, ensuring sustained diversity.
Sim-to-Real Transfer. The best-ranked design from search is fine-tuned with domain random- ization over actuator characteristics, contact and friction parameters, and object poses to improve robustness (Andrychowicz et al. 2020) Tobin al. 2017). For deployment, we train the controller
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Table Comparison of best designs for continuous in-hand rotation in rad/s. Basclines search across 2000 designs with stronger rewards implemented for manipulation (Appendix (955). For the LEAP hand we use the deap-Reorientation environment and train their default policy code and object (1 object). We train another policy our randomized object dataset without object state (blind) as these are representative of our real world task.
Method
Run Time (hours)
Cont. Ang.
Velocity (rad/s)
Ours
6.48
3.3
Ours w/o fine tuning
5.18
1.85
Ours w/ MPPI
20.0
0.62
Leap, Single Cube w/ Vision
2.0
0.47
RoboGrammar
23.0
0.26
Monte Carlo
15.2
0.20
Blind Leap Hand
2.0
0.0
as blind policy, i.e., removing object-state inputs, to match onboard sensing and reduce to-rell mismatch. There no camera or tactile feedback. The corresponding cross-embodiment policy fine-tuned from the family checkpoint using stateless observations. Manufacturing the real robot hand follows the same programmatic grammar used in simulation: the best selected design graph directly converted to modular hardware specifications with 3D- 3D-printed components and precisely matched actuator constraints. A mounting interface is added to the palm, each joint and links are directly 3D printed, and then assembled with Dynamixel actuators. After tuning PID on hardware, the blind policy is deployed on the physical system.
4 EXPERIMENTS
We evaluate our co-design framework on three dexterous manipulation tasks, including one with simulation and real-world deployment for in-hand rotation using generated designs. Our experi- ments demonstrate that cross-ents emboddmebt learning paired with modular hardware graph grammars enables efficient and generalizable co-design for manipulation without sacrificing the runtime or dexterity commonly lost when using traditional co-design control methods. We show successful performance across multiple tasks and demonstrate effective sim-to-real transfer.
Environments. We test the co-design framework across three manipulation tasks, including sim- to-real in-hand rotation. These tasks comprise in-hand rotation, grasping on table top, and object flipping about the z axis on a table top with fifteen randomized objects. We run 50 iterations with 40 designs each iterations. Each evaluation cycle tests candidate designs in parallel across 2048 randomized simulation environments, including randomized initial joint states, physical parameters, and object type. 6 for setup.
Baselines. We compare our co-design framework against the following baselines for in-hand rota- tion: (1) RoboGrammar (Zhao et al. (020a)). procedural generation algorithm using graph-based design rules and local search optimization for robot morphology design; (2) Monte Carlo Tree Search, a tree-based exploration method that systematically searches the design space by build- ing search tree and using random rollouts for evaluation; (3) Our method with MPPI control; (4) The LEAP hand with vision trained on a single object trained with PPO Shaw et al. (202)); (5) A blind LEAP hand with our randomized objects for comparison to standard hardware.
Compared to these baselines, our method achieves finer control and is able to successfully complete manipulation tasks. Robogrammar achieves best design rotation speed of 0.26 rad/s and time to fall of 4.63 seconds for the in-hand rotation task. By contrast, our best designs without fine tuning achieves 1.85 rad/s in simulation with time to fall over the evaluation time of three minutes. Monte Carlo performs worse than the graph heuristic search, with a time to fall of 2.71 seconds. We find reinforcement learning is substantially better suited for manipulation co-design tasks it can better overcome sparse rewards, long horizon tasks, and achieve complex finger gaits needed for true dexterity.

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Figure 4: Top Left: Three finger co-design hand, best design found by algorithm. Top Right: Five finger symmetric co-design hand. Bottom Left: Four finger co-design hand with thin fingertips. Bottom Right: Five finger anthropomorphic baseline.
Sim-to-Reat Transfer. To demonstrate our framework's real-world applicability, we conduct zero- shot blind deployment from simulation reality. We use the best found design deploy a blind policy with our random rotation objects within our domain randomization constraints. Three co- design embodiments are shown show hardware tractability of our modular setup and show design tradeoffs. One baseline anthropomorphic hand is also deployed sim-to-real for comparison. We deploy the policy zero-shot, closed loop with proprioception without state feedback of the object type/position. Only encoder information of servo motors and the morphology encoding are used as the observation.
5 RESULTS: ANALYSIS OF GENERATED ROBOT HANDS
5.1 REAL WORLD DEPLOYMENT
To show our method is able to be built and trans- ferred to the real world, we tested seventeen un- seen objects with variable friction, compliance, and shape. blind policy was trained in simulation on subset of high performing designs found by rota- tion simulated environment. We compare three co- design hands, including the best found optimal 3- fingered hand, to an anthropomorphie baseline. We find robot hands with radial symmetty greatly out- perform anthropomorphic robot hands on blind in- hand rotation. We choose these designs to show the impact morphology on task success and the abil- ity to accurately predict task success ranking in real. From simulation results, the anthropomorphic robot hand trained across the randomized objects achieves a 0.73 rad/s in simulation. The three fingered robot hand achieves 1.85 rad/s without fine tuning. The robot hands were deployed with the morphology-condiite conditioned embodimen policy. The inputs are the morphology's 's encoding and the joint state positions only, with no state informa- tion, while the robot hands ran across objects until either reaching minute of continuous rotation or failure.
Table 2: End-to-end pipeline timing for gen- erating. manufacturing, and deploying one robot hand sim-to-real.
Component
Time (hours)
3D Printing
12.0
Algorithm
6,48
Assemble
0.8
Sim-to-Reat
2.0
We show the time used to rotate given object 360 degrees, and subset of the results is shown in TableB] Failure to rotate within one minute is denoted as a dash. The three fingered robot hand exe- cuted grasps which were able to generalize more effectively to unseen objects. The anthropomorphic and four finger robot hands were able to blindly rotate a small subset of objects three out of sev- enteen tested objects, predicted to be lower performing by algorithm. Objects often became stuck between finger gaits. Across the same seventeen objects, the three fingered robot hand gen- eralized substantially better generalization. Across all seventeen objects, only failed with one
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Table 3: Four designs are shown in order of simulation success, showing the accuracy of our algo- rithm sim-to-real. Task performance (seconds) across hand types and objects. Dash indicates not achieved.
Pink polygon
Tennis ball
Rubik's cube
Green block
Purple ball
Pink disk
Peach can
Yel.
Hand type
fruit
block
3-finger, optimum
13
9
9
5
15
10
6
Finger
12
18
8
14
18
Finger
10
9
Anthropomorphic
25
Taik Performance
Flaere
Figure Normalized reward of best design for flipping, in-hand rotation, and grasping.
two small between the robots bling gait to reach. The three fingered morphology generalized across irregular objects including pine cones, pentagons, and soft objects. The gait of the three fingered robot hand is three altering diagonals, allowing the object to be rotated with little displacement. See videos for details.
5.2 SIMULATION TASK DESIGN RESULTS
In simulation, deploy across three tasks including grasping, in hand object rotation, and object flipping. Grasping: Grasping is lifting the randomized object to the palm with stable grasp and fixed wrist. Across randomized objects, thinner fingertips were primarily picked with 54% of de- signs having this feature. All best designs had full degrees of freedom per finger (4 servo motors) and five fingers of symmetric and anthropomorphic layouts. These designs complete successful grasp without dropping in under half second. Rotation: In hand rotation had the highest standard deviation, with 25th percentile hands having less than 25 degrees of rotation per second and the top 75th percentile hands more than double (67 deg/sec). Best designs used three fingers achieving without fine tuning 1.85 rad/s across objects and 3.3 rad/s with fine tuning. Standard fingertips worked best with 64% of designs using this attribute. Thinner fingertips resulted in unstable rota- tion. Flipping: Flipping composed of rotating the object around the z axis underhand and requires forward rotation with passing the object back to start position for additional rotation. Best designs (58% all generated designs) used wedged or thin fingertips on one side to lift the object easily and standard fingertips on the other side of the palm to have controlled reset with four and five finger symmetric hands. Across tasks, full degrees of freedom (4 servos per finger) consistently per- formed asymmetric designs, including those with thumb layouts. As this is the hardest task due to fixed wrist and variable object heights, the best designs achieve flipping in less than seconds.
6 WHY MORPHOLOGG?
We conduct parameter analysis for robotic hand manipulation by systematically sampling across 12 critical hardware categories in the Jsaac-Reorient- Ceareent-Cub-LLaap task on a LEAP hand (Shaw et al.) 2023). Our sampling approach used uniform grid sampling with 100 samples per parameter across their physically meaningful ranges (Appendix Fig. 14-16), covering three parameter categories: physics properties (static/yynamic friction, restitution), actuator characteristies (stiffness, damping.
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Figure 6: Left: Grasp task with over minute hold time and quick grasps. Bottom:Flipping task's s best found design with rotation on z-axis with a fixed wrist. Right: In hand rotation setup.
effort limits, velocity limits), and morphological scaling factors (palm width, finger length, mass scaling). Each parameter class was trained using PPO, yielding dataset of 1, 1,200 training runs.
Our analysis (Fig. and Appendix Grid Sam- pling) reveals four parameters with particu- larly strong effects on manipulation perfor-
Parameter- Reward Correlations (Sorted by Reward Seusitivity)
10
mance: finger body length scale showed the strongest positive correlation (r=0.748), while palm width scale exhibited a strong negative correlation (r=00729), suggesting that wider palms impede dexterous rotation. Of these,
Marpholagic el Theuraters
(Currsattion with Browrd
morphology parameters had the highest pos- itive impact. Additionally, two motor con-
trol characteristics, damping (r=-0.476) and dynamic friction (r=-0.559). showed moder- ate negative correlations with performance. This provides a strong argument for co-design since the top four parameters can be optimized Material through control and morphology. properties were found to be of lower impact. non-linear relationship is observed through polynomial curve fitting indicate that optimal manipulation performance occurs within spe- cific parameter ranges rather than extremes. Morphology and control having the most pro- nounced impact on task success compared to material or contact properties.
Restiation
Finperte Angpti
Stade
Diasch Amoots
tye Friezoo
Despiing
Statiess
Paia Walts
Proger Leagts
Vebuaty
mitt
Type
Figure 7: Correlation of physical parameters to improving rotation from Bayesian Sampling on LEAP hand. Parameters of highest impact are morphology and control parameters.
7 RELATED WORKS
Co-Design of morphology and control policy.
Traditional design approaches first fix hardware architecture, then optimize control, co-design treats morphology and policy as a joint space that often outperforms separate optimization (Ha al. 2016 Schaff et al. 2019, Matthews et al. 2023). However, the morphology-polit joint optimization is
high-dimensional (Chen et al. 2021
Previous works focused on optimizing the design only
(Ha

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et al. 2021 Xu et al. 2024 Kodnongbua et al. 2023) based on a fixed control policy. co-design
with a limited design space (Luck et al.) 2020; Islam et al. 2024; Yi et al.
2025] Dong et al.
2024). Most co-design work has remained in simulation due to challenges in sim-to-real transfer
of both control policy and manufacturable designs (Xu et al., 2021a
Xiong et al.
2023 Dong
al. 2023a). While most works focused locomotion (uuan et al.
2021 Wang al.
2023c
Dong et al. (023b), manipulation presents unique challenges due to the large hardware design space
(Piazza et al. al.a|2211111 2019), fine control for contact-rich dynamics (Qi al. 2023; Yuan et al. 2020 Zakka
et al. 2023). Evolutionary algorithms scale poorly to complex Loppon & Pollack,
2000 Cheney et al., 2013). Programmatic spaces, as in Zhao al. (2020a). add compositional
rules for valid topologies, improving sample efficiency and structured exploration. We combine
programmatic design with cross-embodiment learning to efficiently search the joint space without
sacrificing fine motor control.
Fooss-Embodiment Learning Learning policies that generalize across different robot morpholo- gies addresses the sample efficiency limitations of training separate models for each embodiment (Doshi et al. 2024; Gupta et al. 2021 Neill et al. 2024). Current approaches leverage shared neu- ral network architectures with morphology condlltiooe inputs (Huang et al.. 2020 Attarian et al. 2023 Furuta et al. 2022), function approximators that encode embodiment parameters (Zhao et al,,
2020a), morphological pretraining (Li al., 2024 SSiraar & Kriegman 2025). and meta-learning
frameworks for few-shot adaptation to new morphologies (Nagabandi et al. 2018 Wang et al, 2023b). Graph neural networks have shown particular effectiveness for variable-topology robots by learning permutation-invariin representations over robot joints and links, enabling zero-shot trans- fer to previously unseen kinematic structures (Wang al., 20188) Liu et al. 2023 Lu et al. 2025). However, transfer performance degrades significanlly with large morphological differences, partic- ularly in contact-rich manipulation tasks where small design variations can drastically affect policy performance (Doshi et al. 2024).
Sim-to-Real Transfer Distribution shift between simulated training and real-world deployment re- mains challenging especially for co-designed systems (Chen et al.) 2021 Schaff et al. [023) The reality gap stems from complex physical dynamics, control noise, and manufacturing tolerances that compound when both morphology and control are optimized in simulation (Peng et al. 2018 Muratore et al. 2022) Current solutions include system identification techniques that adapt learned policies real hardware through online fine-tuning (Du et al. (021). While recent work combines reinforcement learning with differentiable optimization for locomotion (Xu et al. 2021b), co-design for sim-to-real and dexterous manipulation remains largely unexplored.
8 LESSONS LEARNED AND LIMITATIONS
Through our experiments on cross-embodied co-design for three dexterous manipulation tasks, we find the following: 1) Cross-embodiment enables tractable co-design scaling. Cross-embodiment evaluation provides practical approach to assess designs for complex manipulation without sacri- ficing fine control for computational speed, making co-design optimization feasible for real-world deployment. 2) Realistic grammar-based design enables zero-shot transfer. Grounding design grammars in physical components allows for seamless sim-to-real transfer without additional fine- tuning, as the modular structure maintains consistent physical properties across domains. 3) Task- specific optimization can outperform anthropomorphic design. Our results demonstrate the suc- cess of morphologies optimized for specific manipulation tasks (flipping and rotation). Further research in general aom-anhhropomorpic hands can be explored. 4) Morphology dominates the design space. Among all design parameters, morphology has the largest impact on performance, suggesting that shape and structure should be prioritized over other design considerations.
Limitations. While our framework demonstrates effectiveness across multiple manipulation tasks, several key bottlenecks remain. Our current approach only recomputes palm geometry while keep- ing other components modular and pre-defined, limiting full exploration of the morphological design space. Additionally, individual designs remain optimized for specific tasks rather than generalizing across multiple manipulation collls-incorporatiin multi-task taski design averaging or weighting mech- anisms represents a critical direction for future work. Finally, our framework focuses primarily on morphological optimization while neglecting other critical parameters such as compliance, material properties, and actuation schemes that could further improve manipulation performance.
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Acknowledgment Kehlani Fay is supported by NSF Graduate Research Fellowship under Grant No. and DGE-2545911. This project was supported, in part, by gifts from Amazon, Meta, and Qualcomm. A kind thank you to Lars Paulsen for help rendering mechanical rendering.
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Figure 8: Example rollouts of MPPI/CEM control on the LEAP hand for in-hand rotation. The planners generate jitter-heavy, throwing-like motions that produce brief rotation but fail to maintain stable manipulation over long horizons.
APPENDIX
9.1 CONTROL TRADEOFFS
9.1.1 MPPI FAILURE ON SUFFICIENT HARDWARE
To ensure simple and fast control optimization was not sufficient for long horizon manipulation tasks, additional experiment conducted using MPPI rotate cube using LEAP robot hand which has previously proven to perform in hand palm rotation using PPO Shaw et al. (2023). We ran MPPI and Cross Entropy Method on 5 initial grasp positions all within few millimeters of touching the cube of 0,04 m size and 0,15 kg. Over the tested iterations of 60, 120, and 500 prediction horizon and 40 sample trajectories, no solution found was able withhold a time to fall longer than 3 seconds. We tested across three different objects including the cube mentioned prior, a ball of 0,04 m size and 0,1 kg, and a smaller cube of 0,2 kg. Of these solutions, many were jitter heavy and prone to throwing behaviors to achieve initial short horizon rotation rewards. No successful rotation was completed, especially as horizon time was increased. Altering these controllers to handle sparse rewards and long horizon tasks remain open area of research.
9.1.2 CROSS EMBODIED PPO V. INDIVIDUAL PPO PERFORMANCE
Runtime Analysis. To evaluate the computational efficiency of our cross-embodied approach, we compared it against training individual PPO policies for each morphology. Training single de- sign using PPO required over 26 hours on average. Given these constraints, we were only able to evaluate 20 designs, with average evaluation time of 47 minutes per design. In contrast, cross-embodied evaluation framework evaluated 2,000 designs in just 5.18 hours-a 400x speedup runtime while enabling evaluation of 1,800 additional designs in one-fifth of the time.
Fair Comparison Protocol. To ensure a fair comparison with the PPO baseline, we reduced the training epochs from 1,000 (used for the cross-embodied policy) 350 epochs per morphology. This reduction reflects the empirically observed convergence time for individual hand policies and provides a more equitable runtime comparison.
Performance Results. Despite the dramatic computational savings, our cross-embodied approach demonstrates no noticeable performance degradation compared individually trained PPO policies. Moreover, the cross-embodied policy significantly outperforms Lingle-morpholgg PPO training in several key metrics. For example, when trained from scratch on a single five-finger symmetric hand, PPO achieves 0.56 rad/sec rotation. The cross-embodied policy, deployed with fine-tuning on the same morphology, achieves a 65 % performance improvement. Additionally, the cross-embodies policy exhibits greater robustness, with fewer failures across objects, while single-morhoole PPO policies frequently fail to achieve meaningful rotation on the same training object set. Detailed results provided in the accompanying video.
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Figure 9: Exploded view of modular robot hand, anthropomorphic baseline.
9.2 HARDWARE OVERVIEW
We use Dynamixel XL330-M288-T servo motors to control each joint. The system is fully 3D printed from carbon fiber filament for strength. All components were manually designed, then separated into modular pieces with collision meshes computed. Each hand is approximately 7.5 inches in height and 6 inches in width. For generation, convex palms length 0.08 meters were computed around generated finger placements. Palms are 1.5 inches thick with mounting in the back for directly mounting two rotational servo motors which control 90-degree rotation side to side of each finger. Additionally, each horizontally placed linkage is composed of base mount, motor-controlled 90-degree flexion (forward and back), and a hinge to which modular topology components are attached.
For alterable components, we swap each offset linkage to 0.1 inches, allowing for lengthening of each finger. Similarly, the topmost linkage is of four selected fingertips of choice combined with the topmost hinge. This allows us to combine and print these components at once for quick and easy assembly. For electrical control, allows up to 20 motors to be controlled at once. Many cheap, direct drive, or open-sourced anthro- pomorphic hands do not have five fingers (Allegro, LEAP). Creating our own hardware setup allows a more accurate comparison of anthropomorphic designs while ensuring electrical and mechanical alterations. We chose to create our own hardware in order ensure accurate transfer from simu- lation to real-world application, allow modular components, and ensure the electrical system could change dynamically with designs.
U2D2 power hub with the expansion pack. This
Additional designs can be easily printed for each component, by remounting to the servo horn. This allows for new fingertips, finger shapes, palm shapes, or material properties to be explored in the future. Co-design involves both significant hardware and software design. A hardware and manu- facturing guide is presented on our website. By providing a hardware platform that can be easily adapted, we hope to make co-design research more accessible to the broader research community.
Our robot hand baseline is 10% smaller in length than LEAP and has five fingers instead of four. It is also 25% smaller than Allegro. We maintain the same degrees of freedom per finger as LEAP with a base rotation joint and finger hinges for flexion.
9.3 REAL WORLD SETUP
We test 17 unseen objects using the blind proprioception only policy. No object state given to the policy, only morphology encoding and joint state positions for each finger. No vision object state is given to the system, requiring the robot hands to predict object state from resistance of the combination of encoder resistance values. These objects include soft and rigid boxes, weighted and light objects, irregular shapes, variable friction and surface finishes, and textures. No vision used at deployment.
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In experiments, four robot hands deployed from simulation real. The anthropomorphic robot hand was picked from random from our generated dataset to give comparison to typical five finger, anthropomorphic morphology. The other three robot hands were deployed follows: 3 Fin- ger Robot Hand: the optimal robot hand found by our co-design search, Finger Robot Hand: A well performing hand in simulation show ability accurately predict design ranking order sim-to-real, 4 Finger Robot Hand: a moderate to low ranked robot hand by our framework to show poor performance thin fingertips, and Anthropomorphic Hand: to compare anthropomorphic morphology as a lower ranked rotation design and ability to predict design ranking sim-to-real.
In simulation, the predicted performance for each robot hand 1.85 rad/sec across objects for the finger robot hand. For symmetric, five finger design, predicted performance is 1.57 rad/sec. The four finger rohot hand varied highly on objects due to fingertips causing greater object displacement, but averaged about 0.78 rad/sec in simulation. The anthropomorphic robot hand by contrast receives 0.62 rad/sec sim-to-real. These results show the ability of co-design to evaluate designs successfully in simulation and predict their real world performance.
HABET OUP UN
PEAC
In real, the objects picked ranged from 8g over 100g with varying textures, mass, compliance, and center of mass. Our three finger robot hand was able to generalize significantly better across objects, including rotating a real world pinecone with adaptive gaits to object position. This also best seen by being able to rate the tennis ball for over 10 minutes without failure while other designs cause greater displacement and are unable to predict object state sufficiently for blind rotation to complete rotation for this duration.
9.4 ENVIRONMENTS
We created three simulation environments including object rotation, grasping, and flipping. The results of these be seen in video. Prior works in co-design for manipulation have almost exclusively focused on grasping. However, we find this task had the least sensitivity to design for our setup, compared in-hand rotation and flipping. We think this tradeoff is likely due using rigid bodies and that in-hand rotation and object flipping are dynamic tasks with much more sensitive constraints for fine dexterity.
9.5 STRONGER BASELINES
RoboGrammar PyBullet and reimplement environments in PyBullet to test across. Without these alterations, the method does not work at for manipulation. We alter RoboGram- mer's generation rules and reward function follows:
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Figure 10: Two sampled co-design hands including the 5 finger with standard fingertips and four finger symmetric hand with thin fingertips are deployed sim-to-real. Both are made of the same modular components, but with varying finger lengths, palm shape, finger number, and fingertips.
Figure 11: Modular anthropomorphic robot hand baseline.
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+
Figure 12: The object dataset used for evaluating designs across tasks in simulation. Objects were
domain randomized for scale sim-to-real with size variance of 0.7 to 1.3 times size. Mass, position noise, and additional factors randomized.
Robot Design Rules
Fingertip Shape
Finger Length
Number of Fingers
Number of Joints
Palm Shape
Figure 13: Five parameters are optimized including fingertip type from selection shapes inspired from LEAP Shaw al. (2023), finger length of 1-10 linkage extensions for each joint, finger place- ment and palm shape as determined by convex hull of finger placement, number fingers 3-5, and number of joints 3-5 joints including a default base rotation joint for side to side movement.
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Table 4: Simulation Physics and Actuator Configuration
Parameter
Value
Rigid Body Properties
Static friction
0.2-1.5 (randomized)
Dynamic friction
0.4-1.5 (randomized)
Restitution
0.0-0.5 (randomized)
Object density
1000 kg/m²
Bounce threshold velocity
0.2 m/s
Actuator Settings
Effort limit
0.35 Nm
Velocity limit
7.2 rad/s
Stiffness
2.5-3.1 Nm/rad (randomized)
Damping Friction
0.05-0.15 Nms/rad (randomized) 0.01
Figure 14: Random examples of generated robot hands.
The initial body node was changed to a palm shape, represented by short and wide cylinder. The body node included potential starting points, and the grammar was changed to include the option of adding limbs to these body nodes or terminating them. The angles of the joints were changed face upwards to allow for in-hand manipulation place of locomotion. Otherwise, the grammar rules were unchanged. The reward function consisted of a reward for the angular velocity of the object one direction, a penalty for no motion of the object, and a penalty for object displacement from its initial position. This results in throwing action to spin the cube.
We originally did not account for consistent rotation direction in the reward, which performed better but resulted in back-and-forth motion.
9.6 MORPHOLOGY GRID SAMPLING
By sampling simulation physical parameters of the LEAP hand we find that relative morphology matter significantly. We suspect that damping and dynamic friction also play large role but become harder to control with standard control rewards and have difficulty in correctly modeling soft bodies. These two areas remain open for further research.
10 REPRODUCIBILITY STATEMENT
All work can be reproduced by utilizing the open sourced code and designs. Current hardware is fully 3D printable and all electrical equipment including Dynamixel motors which have become
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A.
C.
B.
D.
Figure 15: Design generated using RoboGrammar. RoboGrammar with direction-aware reward, B. RoboGrammar without directional information, C. RoboGrammar with Monte Carlo Tree Search and direction-aware reward, D. RoboGrammar with Monte Carlo Tree Search and without diree- tional information.
Finger Body Length Scale Correlation: 0.748, N=100
Palm Width Scale Correlation: -0.729, N=98
Episode Return (Last 10 Iters)
Episode Return (Last 10 Iters)
Last Beeatrons Reward
60000
iterations Reward
3000
40000
2000
.0000
20000
1000
10000
Finger Body Length Scale
Palm Width Scale
Damping
Dynamic Friction Correlation: -0.459, N=100
Correlation: -0.476, N=100
Episode Return (Last 10 Iters)
40000
Last Iterttoons Seward
80000
iterations Reward
Episode Return (Last 10
500000
.00000
40000
20000
30000
10000
10000
0.75
Damping
Dynamic Friction
Figure 16: Top four highest parameters of impact with 100 samples completed for each parameter. For each sample, PPO policy is retrained with an updated LEAP hand for the new hardware parameter. This then tested for for in-hand rotation.
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Cata Siiiic
1452
14
Conmrnnn
201
120
Figure 17: All sampled parameters using Bayesian sampling on the LEAP robot hand and found correlation to rotation reward.
Parameter Reward Cocrelations
Reward Scasitivity by Parameter
(Sorteel by Reward Sounstivlit)
(Morted by Correlation)
16-1
204
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7-
[Cormiitton] with Neewort
6-
Deward Rauggo (Max (420))
9.
1-
Restiittin
Flegeriy Lomelt
Whete Linit
Pretios
Dagrnto
Atatioee
Pats Wtlas
Plager Lengls
Proserts Langst
Wennaty Line
Thankh August
suttone
Pats sois
Pageer Lnngle
Hara
Trank
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Figure 18: Ranking of each found parameter on reward impact and sensitivity of each parameter to change in reward. Results from Bayesian sampling.
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Table 5: Adaptive Domain Randomization Parameters
Parameter
Range/Value
Object Randomization
Mass scaling
0.9-1.3x
Size scaling
0.7-1.3x
Position noise
0,0-0.01
Rotation noise
0.0-0.1 rad
External force acceleration
0.5-5.0 m/s?
Robot Randomization
Joint position noise
0.0-0.05 rad
Joint velocity noise
0.0-0.01 rad/s
Action noise
0.1-0.2
Action latency
0-3 timesteps
ADR Progression
Number of increments
25
Minimum rotation coefficient
0.15
Minimum steps for change Starting increments
960
increasingly utilized by labs for under $1,500 USD. Code for training existing tasks is documented alongside control for real world deployment. The authors have included instructions for adding new environments for further testing generalization capabilities, adding variable hardware and morphol- ogy constraints, and overcoming common sim-to-real issues. These efforts allow the work to be reproduced in any lab with a 3d printer and one stereo camera. Experiments can be reproduced for any individual task.
11 ETHICS STATEMENT
Our work completes sim-to-real co-design generation for real world robot manipulators. We hope through these efforts that positive impact such as designing more capable prosthetic hands for hu- man neural control, pushing the understanding of tradeoffs in robot hardware, and achieving higher dexterity through co-design can be effectively explored. We acknowledge the development of more complex dexterous robot hands may enable effective automation of existing roles. We ask this search to be used for only academic and open source purposes and for users to utilize this framework method evaluate tradeoffs in design and control decisions. Materials used for manufactur- ing primarily relied on PLA which is not widely recyclable except under industrial compostable conditions. This work significantly reduces required compute for co-design reducing training from over 100 days worth of single compute time using individual PPO policy for each design to 1.75 kw hours on RTX 3090. This work utilizes approximately 0.6 kg of CO2 which equivalent to approximately 56 smartphone charges.
12 LLM ACKNOWLEDGMENT
A large language model, specifically ChatGPT 4.0 was used in polishing the writing of this paper. In particular, spelling, typos, and grammar structure were aided by GPT through all sections of the written manuscript. The authors wrote the first drafts without the use of GPT and only in final stages used GPT for polishing. Additionally, GPT was used to clean and polish our code base in the supplementary materials. This aided in formatting and removing deprecated code.
23

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Published as a conference paper at ICLR 2026
Algorithm 1 Cross-Embodiment Graph Heuristic Search
Require: N, K, KK,(0uuuere To, S,
Ensure: G*, R
Initialize Vy. [(: t- x0, D + G + 0,
2: for 1 to N do
3: d; <- CURRENTGROUP()).
0
4: for k = to do
5: Tk 5 To (k: 1)AT, GETINITIALDESIN((..))
6:
while -ISCOMPLETE(G) do
7:
Ω + OPTIONS(
8:
o* +- arg maxxen (Vo (APLLAPPLYRULE(C..))) u:) TxEa), &o Gumbel 1) G + OF
9:
end while
11:
kG + ELFECTIVEKEY(G, u4)
if ki is unique C, then
13:
C; + C; U [CANONICAALIZE((]]]
end if
15:
end for
R, < CROSSEMBODIDDEEVLLLTECC,,, O..)) (evaluation done in parallel)
17:
for all (G (C,, R,) do
18:
Ky + EFFECTIVEKEY(GJ, uz). Tik,, uz] + max uz], ry) D DU [(G; ry)
19:
20:
for all E PARTTALANCESTORS(C
do k < EFFECTIVEKEY (A, u;), A ui] max(T(ka u4),
21:
22:
with prob. p. add (A, u, Tÿ) to D end for
23:
24:
if > R then
G G R
+ Tj
26:
end if
end for
28:
if [
B then for s to S do
29:
30:
Sample minibatch B c D. B - Update on (VC..r)e(VV(ll(G)......
31:
..B
end for
33:
end if
end for
35:
return R*
24


Reviews:
[{"review_id": "zFa5r04yc2", "reviewer": "Reviewer_P9ND", "summary": "This paper presents a co-design framework that jointly optimizes dexterous hand morphology and control policies for in-hand rotation tasks. The approach uses morphology-conditioned policies, graph neural networks for design encoding, modular hardware grammars and graph heuristics search. The framework is evaluated in both simulation and the real world, demonstrating that optimized designs outperform baselines.", "questions": "- The run time reported in table 1 (i assume is the time for each approach to search for one design) is very slow. Have the authors considered using optimization methods (zero order such as CMA-ES, bayesian optimization and first order such as gradient descent, L-BFGS, diffusion guidance) rather than search based algorithms?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The use of cross-embodiment control policy for task-specific hardware design is well-motivated.\n- Experiments are done both in sim and real, especially with solid real world results.", "weaknesses": "- The task is a bit limited and narrow - focusing solely on in-hand rotation.\n- The method uses a search-based approach to find optimal designs, which is very slow. Also the graph heuristic search explores only 50 iterations × 25 designs = 1,250 total designs, which seems limited given the design space complexity.", "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": "VB4VRQ1X9X", "reviewer": "Reviewer_hBj2", "summary": "The authors introduce a co-design framework that jointly optimizes the control policy and dexterous hand morphology given a specific task. This problem is high dimensionality in both the policy and manipulator space. The authors claim that by grouping manipulator morphologies  into classes they address this issue. As far as I can tell this has not been shown empirically or theoretically. However, the experimental analysis does indicate the utility of the co-design strategy. For example, different manipulator morphologies are identified for in hand rotation and flipping.", "questions": "**Questions/Comments**\n- Line 096: what are X_v and X_e here? The feature on each node and edge?\n- As I understand, a GNN wouldn’t be able to differentiate between a hands with finger arrangements related by permutation. This seems undesirable since a radially symmetric five fingered hand with two shorter fingers next to each other would likely benefit from a different control policy than a hand with two shorter fingers separated by two longer ones.\n\n**Possible typos**\n- Line 269: These tasks comprise → These tasks include\n- Table 1: It would be a little easier to read if the strongest performing results were in bold\n- Table 1: It seems like this should be two separate tables?\n- Line 300: Robogrammar achieves an average rotation speed of .367… This value is different than what is reported in Table 1\n- Line 320: anc → and\n- Line 321: radial symmetrical → radial symmetry\n- Line 324: 1.7 radians/sec → 1.7 rad/s. (so the notation is consistent)\n- Line 354: secventeen → seventeen\n- Line 137: G\\in \\mathcal{D} → G\\in \\mathcal{G}", "limitations": "", "rating": 4, "confidence": 2, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "**Originality.** The work appears original. \n \n**Quality.** The work appears to be of good quality. The work is well motivated (co-design is very high-dimensional), but it is not clear to me how the approach addresses the challenge indicated in the motivation. \n \n**Clarity.** The paper is well written and well organized.\n \n**Significance.** The significance of co-design is clear, and the authors demonstrate that the proposed approach is an effective strategy for co-design.", "weaknesses": "My main uncertainty about this paper is whether the claim that the proposed approach reduces the dimensionality of the problem has been shown.", "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": "uzq8ArXOjw", "reviewer": "Reviewer_LTDD", "summary": "This paper aims to address the challenge of co-designing dexterous robotic hands, where both the physical morphology and the control policy are optimized simultaneously. The authors leverage a cross-embodiment control policy to overcome the computational intractability of this problem. This universal policy is pre-trained on a diverse set of robot hand designs generated by a procedural grammar. Morphologies are encoded using GNNs to condition the policy, and a learned design value network guides the search for optimal designs. The framework is validated on several simulated manipulation tasks and through the fabrication and zero-shot deployment of an optimized 3-fingered hand.", "questions": "See weaknesses.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "- The paper’s strongest aspect is its rigorous validation on a full-stack physical platform from hardware design to policy deployment.\n- The proposed method directly tackles the computational cost of searching the joint design-control space in co-design tasks. The framework's total design-to-deployment time of under 24 hours is a significant engineering achievement and a key selling point for its real-world utility.\n- The work provides valuable insights that extend beyond the specific designs found, contributing to the broader understanding of dexterous manipulation.", "weaknesses": "- The bi-level optimization problem in Eqs. (2)-(4) implies a joint optimization, but the described method involves first pre-training a general policy and then using it for evaluation. The connection between the formal problem and the practical algorithm is not well-established, making the algorithm feel more like an effective heuristic than a direct solution to the stated objective.\n- The real-world validation does not compare the co-designed system against a sufficiently strong, specialized baseline, making claims of overall system superiority difficult to substantiate. The experiment compares the optimized 3-fingered hand and the baseline anthropomorphic hand by deploying the same morphology-conditioned cross-embodiment policy on both. This effectively tests which morphology performs better given the proposed adaptive control framework. This comparison does not rule out the possibility that a state-of-the-art policy, trained specifically for the anthropomorphic hand, could outperform the co-designed system. The anthropomorphic hand's failure could stem from the universal policy's inability to master its higher-DOF control space, rather than a fundamental flaw in the hand's design for the task. To make a stronger claim about the superiority of the final co-designed system, the baseline should be a strong, existing design/policy pair (e.g., an anthropomorphic hand with a dedicated, highly-tuned policy), not just a standard design controlled by the paper's own universal policy.\n- Table 1 should be split into two tables. It combines two conceptually separate tables into a single, side-by-side layout under one caption. It is confusing whether the rows are meant to be read horizontally across the entire table or as two independent blocks.", "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": "5wDIgjFgIV", "reviewer": "Reviewer_KFiu", "summary": "The paper explores the problem of the morphology-policy co-design in dexterous in-hand rotation. The problem is formulated as a bi-level optimization problem where the optimization space includes both the hand morphology and the control policy. To solve the problem, a cross-embodiment control policy is first pre-trained based on sampled morphology designs. After that, the pre-trained policy is leveraged to provide feedback for sampled designs. The cross-embodiment policy is further fine-tuned based on morphology samples. Iterating the co-design and evaluation loop helps converge on the final outcome.", "questions": "- Why do real-world videos only present three-fingered hands and five-fingered hands? What does this video attempt to convey? -- It seems that in some cases, the hand fails to rotate the object. \n- What about the performance of this baseline -- using the leap hand rotation codebase to train a policy for each specific searched hand morphology?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- Well-motivated problem. How to increase the optimization space from pure control policies to both the control policy and the robot morphology is an important problem in robot learning. The paper is targeted at the in-hand object rotation task and proposes to design both the hand morphology and the policy. \n- Reasonable methodology. Bi-level optimization is a reasonable formulation for the co-design problem. Iterating between the policy optimization and the morphology design space searching is a reasonable solution for the nested problem. \n\n- Compared to prior co-design works, this one conducts real-world experiments and evaluations, which increases the value in real-world applicability.", "weaknesses": "- Spurious effectiveness. Object-in-hand rotation is a widely explored problem in the dexterous manipulation community. This work presents a research direction where the control policy and the hand morphology are co-designed. However, how does its effectiveness compare to prior in-hand rotation works, like Hora or Leap hand rotation (from Kenny Shaw). I cannot see improvements solely from the results achieved by this paper compared to prior in-hand rotation works.\n- Limited analysis of the design space. The paper lacks a thorough in-depth analysis of the morphology design space, e.g., using what designs lead to satisfactory performance, and using what designs is not sufficient for a good optimization? \n- Unclear methodology. How do authors iterate between the morphology design space optimization and the cross-embodiment policy training? It seems that section 3.1 does not contain this part. \n\nOverall, this work explores an interesting direction. However, the insufficient comparisons and unclear parts in the methodology make its effectiveness and its positioning in the literature quite spurious.", "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)