Authors: Maximilian Stölzle, Niccolò Pagliarani, Francesco Stella, Josie Hughes, Cecilia Laschi, Daniela Rus, Matteo Cianchetti, Cosimo Della Santina, +1 more
Organizations: Laboratory for Information & Decision Systems, Massachusetts Institute of Technology, Cambridge, 02139, MA, USA. · Cognitive Robotics, Delft University of Technology, Mekelweg 2, Delft, 2628 CD, Netherlands. · Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, 02139, MA, USA. · The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, 56025, Italy. · Embodied AI AG, Lausanne, 1015, Switzerland. · CREATE Lab, EPFL, Lausanne, 1015, Switzerland. · Advanced Robotics Centre, Department of Mechanical Engineering, National University of Singapore, 117575, Singapore.
Soft robots promise inherent safety via their material compliance for seamless interactions with humans or delicate environments. Despite progress, the field struggles to balance task-specific performance with broader factors like durability and manufacturability--a difficulty that we find is compounded by traditional sequential design processes with their lack of feedback loops. In this perspective, we review emerging co-design approaches that simultaneously optimize the soft robot's body and brain, enabling the discovery of unconventional designs highly tailored to the given tasks. Their adoption is limited by narrow objectives, gaps between simulated and real performance, and computational cost. To address these challenges, we propose a holistic co-design framework that incorporates a broader range of design values, integrates real-world prototyping to refine evaluations, and boosts efficiency through surrogate metrics and model-based control strategies. Finally, we outline research priorities in design priors and metrics, AI-assisted evaluation, and balancing computational refinement with physical testing and safety with performance.
Figures & tables
Figure 1: Holistic Co-Design of Soft Robots. The five pillars of holistic co‑design are (1) Incorporating comprehensive design requirements and values directly into multi‑objective optimization; (2) Efficiently exploring the full design space by melding design priors (e.g., biological inspirations, existing solutions) with computationally efficient co‑design routines; (3) Explicitly accounting for design realization (e.g., prototyping and testing) via a probabilistic treatment of evaluation metrics and by formalizing the refinement‑vs‑realization trade‑off—enabling targeted prototyping to reduce metric uncertainty and narrow the sim‑to‑real gap; (4) Ensuring auditability to preserve design knowledge and guarantee reproducibility. (5) Fostering cross‑disciplinary collaboration and involving all relevant stakeholders in defining design values and providing iterative feedback; These pillars are inherently interconnected (see green arrows)—for example, stakeholders and the design team co‑define design requirements, certain design values (e.g., HRI) demand evaluation in the real world (i.e., enabled by realization), and both the exploration of the design space and the fulfillment of design requirements remain fully auditable.
Figure 2: The traditional soft robot design process vs. holistic co-design. Left: A traditional sequential design cycle van Boeijen et al. (2020) applied to soft robots. This sequential workflow lacks iterative feedback loops, leading to information silos and preventing regular, bidirectional sharing of insights and data across the development team and the relevant stakeholders. Right: A holistic co-design framework that complements computational refinement with prototyping across various levels of design readiness - visualized as “onion” layers in this graphic. Solid yellow lines denote the prototyping pathway, while dashed orange arrows illustrate how empirical findings continuously inform and update the estimates c^j for the real‑world design values cj .
Figure 3: A Framework for Efficient Computational Co-Design of Soft Robots. We sample reduced-order design parameters x from an initial distribution that can include design priors from biological inspiration Mazzolai and Laschi (2020) ; Chen and Wang (2020) ; Laschi and Mazzolai (2024) , known mechanisms, or existing soft robot designs. This design space—either learned or explicitly defined by physical or geometric values (i.e., size optimization)—is translated by a design decoder into a detailed robot body description (such as a 3D mesh with sensor and actuator placements) that provides immediate manufacturability feedback. A reduced-order model is then derived Alora et al. (2023) ; Stölzle and Della Santina (2024) ; Valadas et al. (2025) ; Alkayas et al. (2025a) or learned to efficiently assess workspace, open-loop compliance, and embodied intelligence Cianchetti (2021) ; Mengaldo et al. (2022) ; Vihmar et al. (2023) , offering inexpensive feedback to the optimizer. Similarly, perception and control systems can be developed using this model (via model-based control Della Santina et al. (2023) , MPC Alora et al. (2023) , or differentiable physics Spielberg et al. (2019) ; Wang et al. (2023b) ; Stölzle et al. (2026a) ), while observability and controllability are evaluated without costly simulations. Finally, closed-loop simulations (low-fidelity using the reduced-order model or high-fidelity FEM-based Coevoet et al. (2017) ) assess integrated performance metrics, which in turn optimize the sampling distribution, decoder, and all relevant system parameters.
Figure 4: Refinement vs. Realization. Top Left: This panel shows how refinement affects the design sampling distribution ϑ(x) . Unlike (most) conventional co‑design methods that sparsely explore the design space, we condition ϑ(x) on design priors (e.g., biological inspiration and existing soft robot designs) and iteratively update it via the co‑design optimizer until the optimum is reached. Bottom Left: Here, we demonstrate how iterative realization refines probabilistic evaluation metrics. Rather than using deterministic metrics that ignore inherent simulation uncertainty, we define a probabilistic distribution m^j(c^j,x) for each metric, with purposeful prototyping reducing the uncertainty over iterations. Right: This panel examines the tradeoff between refinement and realization. We define the realization ratio RealR as the fraction of realization iterations relative to the total iterations and the sim‑to‑real error as ∥cj−E[c^j]∥ . We then plot the evolution of real-world cost cj versus sim‑to‑real error as iterations increase for a fixed RealR : when limRealR→0 , designs are optimized computationally with realization occurring only at the end, while RealR=1 means only the confidence in the evaluation metrics is increased. The optimal ratio RealR∗ efficiently minimizes the real-world cost to its optimum minxcj(x) via refinement and reduces the sim‑to‑real error to zero by updating m^j(c^j,x) via realization.
Supplementary Fig. 1: Illustration of the proposed safety metric for quantifying the injury severity of a local blunt contact between a soft robot and a human body part. The maximum contact pressure/stress maxtpc(t)=maxtfc(t)/Ac(t) is used as a proxy for injury risk International Organization for Standardization (2016) . The contact force fc(t) is obtained from a one-dimensional projected collision model along the contact normal nc , with penetration coordinate δc(t) . To obtain a conservative estimate, the human-side motion is prescribed, corresponding to the limiting case in which the human body part is effectively constrained or has inertia much larger than the reflected robot inertia mR .
Metric
Experimental protocol
Computational implementation
Pick success rate
Count successful picks over total attempts in greenhouse trials.
Simulate grasp dynamics and apply success probability models under domain-randomized variability.
Selectivity error (ripe vs. unripe)
Compare picked fruits with human-annotated ripeness ground truth.
Classifier accuracy on synthetic or augmented datasets representing different ripeness states.
Cycle time
Measure time per pick from actuation start to fruit release.
Model task scheduling with actuator and robot dynamics; stochastic timing models.
Throughput per hour
Number of fruits successfully harvested per hour in pilot trials.
Extrapolation from cycle time distributions under randomized task sequences.
Dropped fruit percentage
Ratio of fruits lost during transfer to total picked fruits.
Simulated grasp stability analysis with randomized external perturbations.
Bruise percentage (24/48h)
Visual inspection and pressure mapping after storage at 24/48 h.
Contact force simulation with tissue deformation models and probabilistic damage thresholds.
Supplementary Table 1: Probabilistic evaluation metrics with experimental protocols and computational implementations.
Co-design is a high-dimensional search problem in the robot morphology and control design space. Efficient search requires exploiting the structure shaped by their interaction. To understand this structure, we analyze the landscapes of soft locomotion and manipulation tasks. We identify three patterns consistent across regions of their co-design spaces: 1) Within a region, quality varies along a low-dimensional manifold, with minimal variation orthogonal to it, reducing the effective search space dimensionality. 2) In higher-quality regions, the variance in quality is spread across more dimensions, necessitating search to expand dimensionality as quality improves. 3) In higher-quality regions, quality varies along joint morphology-control dimensions, requiring search along them. Using these insights, we devise an efficient co-design algorithm that yields 36% better co-designs than state-of-the-art baselines. We examine their exploration patterns and show that these baselines required an order of magnitude more function evaluations to find co-designs of comparable quality. Finally, we ablate our algorithm to verify that exploiting the identified structure was the key to efficient co-design.
Apoorv Vaish, Oliver Brock
Robotics and Biology Laboratory, Technische Universität Berlin · Science of Intelligence (SCIoI), Cluster of Excellence, Berlin · Robotics Institute Germany (RIG)
Soft wearable robots have evolved rapidly from proof-of-concept devices into promising platforms for rehabilitation, occupational assistance, and human augmentation. As the field matures, its central challenge extends beyond the development of softer materials and more capable actuators to the integration of sensing, intelligence, and human adaptation into systems that users can wear comfortably, trust, and benefit from over extended periods. This transition motivates the concept of Human-Centric Embodied Intelligence (HCEI), in which intelligence emerges from the coupled human-robot system through the interaction of morphology, multimodal sensing, adaptive cognition, compliant actuation, and the wearer's own physiological and behavioral adaptation. To organize this perspective, this review introduces the Perception-Cognition-Actuation-Augmentation (PCAA) framework, which positions perception and cognition as the primary drivers of design, shifting development beyond the conventional actuator-first paradigm. Using this framework, the review synthesizes advances in soft materials, wearable sensing, artificial intelligence, actuation, human-robot interaction, digital twins, clinical translation, manufacturing, regulation, and ethics, highlighting how these interdependent components collectively shape long-term personalization and real-world deployment. By providing a unified conceptual framework and design perspective, this review aims to guide future research, foster interdisciplinary collaboration, and accelerate the translation of next-generation soft wearable robots toward personalized, predictive, and human-centric wearable intelligence.
Rainier Natividad, Raye Chen-Hua Yeow
Department of Biomedical Engineering, National University of Singapore
Designing multi-agent robotic systems requires reasoning across tightly coupled decisions spanning heterogeneous domains, including robot design, fleet composition, and planning. Much effort has been devoted to isolated improvements in these domains, whereas system-level co-design considering trade-offs and task requirements remains underexplored. In this work, we present a formal and compositional framework for the task-driven co-design of heterogeneous multi-robot systems. Building on a monotone co-design theory, we introduce general abstractions of robots, fleets, planners, executors, and evaluators as interconnected design problems with well-defined interfaces that are agnostic to both implementations and tasks. This structure enables efficient joint optimization of robot design, fleet composition, and planning under task-specific performance constraints. A series of case studies demonstrates the capabilities of the framework. Various component models can be seamlessly incorporated, including new robot types, task profiles, and probabilistic sensing objectives, while non-obvious design alternatives are systematically uncovered with optimality guarantees. The results highlight the flexibility, scalability, and interpretability of the proposed approach, and illustrate how formal co-design enables principled reasoning about complex heterogeneous multi-robot systems.
Maximilian Stralz, Meshal Alharbi, Yujun Huang +1
Laboratory for Information & Decision Systems, Massachusetts Institute of Technology, Cambridge, MA 02139