Researchers envision a future where groups or individual soft robots not only perform their intended tasks but also do not harm the ecosystem throughout their entire lifecycle, thus supporting the principles of SDG-12. To achieve this goal, we believe it is essential to use biodegradable and soft multifunctional materials, whose physical and chemical properties can be manipulated to ensure intelligence while minimizing their ecological impact (Rossiter et al., 2016; Mezzenga, 2021). Families of polymers such as natural fibers, liquid crystal elastomer, and thermoplastic polyesters could be adopted as constitutive multifunctional materials for C2C robot implementation in energy generation/saving, visual responses to external/internal stimuli capabilities etc. (Shintake et al., 2017; Prévôt et al., 2018; Zaia et al., 2019; Mishra et al., 2020).

As a futuristic idea, we conceptualize a cephalopod-inspired soft robot, designed to camouflage in its surroundings to study the behavior and biology of lone aquatic animals, thus assisting in ocean conservation and management (SDG-14). Its biodegradable constitutive materials may implement intelligence such as adaptive color changing while performing continuous deformation and texturing of the body for safe interaction with living beings (Clough et al., 2020; Giordano et al., 2021a). Consistently, a system of biodegradable jellyfish-inspired robots that wirelessly and autonomously communicate may follow sea currents to gather data and develop science-based management plans aimed at restoring populations of species that are at risk due to climate change factors like acidification and nutrient depletion (Christianson et al., 2019). We believe that soft robots that are smart, biodegradable, and capable of couple sensing, actuation, computation, and communication have the potential to support SDGs 11, 13, 14, and 15 (McEvoy and Correll, 2015; Garrad et al., 2019; Kaspar et al., 2021; Meder et al., 2022). However, it is essential to shift toward a paradigm that prioritizes nature-based solutions and innovative mechanical design as central elements (Pishvar and Harne, 2020; Khaheshi and Rajabi, 2022). By extracting key parameters for multifunctionalities from natural systems that have evolved over millions of years, we can optimize the design and material of these robots to reach their maximum potential and the less consumption of raw elements (Kolle and Lee, 2018; Phillips et al., 2022).

The vision of sustainable manufacturing is to create “printed future” in soft robotics, and the main advantage of additive manufacturing technologies (i.e., 3D printing) is to integrate actuators, sensors, controllers, and power systems into fully autonomous soft robots through a single, on-demand digital process (SDG-12) (Wallin et al., 2018; Helou et al., 2022; Leber et al., 2022). The “Emb3D printing” technique is an example of 3D printing to manufacture advanced soft robots (MacCurdy et al., 2016; Wehner et al., 2016). This method has been used to create untethered octobot robots that can incorporate drive systems, actuators, and soft microfluidic chip, thus showing a cost-effective solution for large-scale monitoring and swarm robotic applications. Furthermore, some robots integrate 3D printers in their body which allow them to change shape based on sensor feedback to change shape based on sensor feedback (Shah et al., 2020). For example, Sadeghi et al. (2016) developed a robot that can grow its shape through selective deposition of a biopolymer, and with several sensors at the tip, it can search for water and nutrients underground (SDG-6, 15). Recent advancements in 3D printing technology have led to the development of a rotational multi-material extruder that allows for precise control over the local orientation of architected filaments (Larson et al., 2023).

The authors envisage for future MIS applications a 5D-printed soft robot with shape-shifting capabilities that can integrate multifunctional and stimuli-responsive materials together with sensors, actuators, and artificial intelligence (AI)-driven control systems in a single manufacturing process (Pugliese and Regondi, 2022). We conceptualize the manufacturing of a biocompatible and biodegradable soft robot made of hydrogel material that can be programmed to change stiffness and shape in response to specific stimuli, such as temperature or pH. Thus, it will navigate through the body, conform to tissue, avoid obstacles, transmit data, and safely degrade. To date, few examples of existing technologies that can print multidimensional soft robots are reported by Barner-Kowollik et al. (2017), Carlotti and Mattoli (2019), and Hippler et al. (2019). Shi et al. (2019) showed a soft robot for MIS embedded with responsive materials that can be remotely controlled to perform delicate procedures, reducing invasiveness and recovery time (SDG-3, 12). Advances in sustainable manufacturing, design, and materials are driving soft robotics toward impactful solutions to achieve SDGs and aid the climate transition (Stella and Hughes, 2023).

The integration of renewable energy sources in soft robotics technologies holds great promise to support SDG-7 and 9. However, incorporating these sources into the soft and flexible body of the robot and developing efficient and lightweight energy storage and sourcing solutions that are able to fulfill their energy consumption are significant challenges. Research is being carried out on flexible and organic photovoltaics (PVs), 2D–3D nano-heterostructures, triboelectric micro/nano-generators (TENG), and fuel cells that generate power from renewable sources (Pomerantseva and Gogotsi, 2017; Chen et al., 2021; Hwa Jeong et al., 2021; NG et al., 2022). Great potential is on the adoption of portable, versatile, and flexible perovskite-based PVs that may find application in self-piloting drones, robots, and wearables, enabling the robot to be self-sufficient in terms of energy (Bierman et al., 2016; Horne et al., 2020; Rombach et al., 2021; Hou et al., 2022). However, to meet the goal of SDG-7, we suggest robots must be equipped with combined solutions. For example, in marine environments, a tethered solution can be used with a harvester and generator beyond the ocean surface and an inner body electro-/photo-catalytic proton exchange membrane cell to produce hydrogen for power generation (Kuang et al., 2019; Lü et al., 2022). This approach would allow for the camouflaged/swarm robotic systems to autonomously cover large kilometer squares for precise data acquisition (SDG-14).

To date, the scientific community is working toward reducing the energy consumption per robot task. TENGs have shown promise in this regard, but for consistent and efficient energy design, detailed studies are needed (Liu et al., 2021; Pang et al., 2021; Chen and Wang, 2022). The amount of energy consumption during specific tasks or activities such as movement or data collection must be identified. However, we deem that the future goal for energy autonomy in variable environments will be to replicate artificial photosynthesis by coating the skin of robots (McFadden and Al-Khalili, 2016; Chen et al., 2022). In the near term, we should focus on developing lightweight and integrated devices to meet the energy needs for autonomy through renewable energy sources, reduce energy consumption per robot task, and effectively conserve, distribute, and balance energy supplies in various environments (Phillips et al., 2018).

The implementation of distributed sensing systems, utilizing a group of specialized soft robots, can provide early warning for extreme events, prevent disaster, and improve forecasting models, contributing to sustainable cities and communities (SDG-11) and combating climate change (SDG-13) (Bergstrom et al., 2021; Spillman et al., 2021). We proposed biodegradable, self-governing soft machines that can survey large areas of the ocean and gather data for informed decision makers in compliance with SDG-17. The group of robots endowed with an efficient network of sensors and detectors, integrated onboard or within the constitutive materials, may report data on non-surfacing marine beings, rising temperatures, changing currents, and plastic degradation (SDG-14) (Yeh et al., 2016; Herbert-read et al., 2017; Jaffe et al., 2017; Picardi et al., 2020). Nonetheless, we envisage other roles for these machines that could survey deep sea pools to retrieve raw minerals for sustainable industry (SDG-9) (Yang S. et al., 2018; Dini et al., 2022).

In terrestrial environments, the integration of soft robots with satellites and traditional robotics can provide solutions for archaeological and heritage conservation (SDG-15). The inherent soft robotics dexterity makes them well-suited for environments of ruins, where they can safely navigate and manipulate fragile artifacts. Consistently, adopting terrestrial and aerial robotic solutions, we could gather data for seed plantation toward reforestation against desertification to capture carbon dioxide, or to feed population and local economies, and to evaluate new pathogens and vector-borne diseases (SDG-3, 8, and 13) (Wade et al., 2018; Chowdhary et al., 2019; Zhou et al., 2022). C2C self-burial systems or climbing micro-soft robots could be adopted on crops to gather targeted genetic information and provide precise monitoring and surveillance of plants and soil (SDG-8, 12, and 15) (Fiorello et al., 2021; Horton et al., 2021; Mazzolai et al., 2021). Furthermore, C2C micro-robots can be released in water sources and infrastructures to detect pollutants and/or nutrients (SDG-6). In sustainable cities, drones and climbing soft robots, in conjunction with 3D scanning, will enable point-in-time inspection and monitoring of buildings or bridges, modifying planned task in real-time (SDG-11). Also, in space, monitoring and harvesting debris can be effectively achieved using soft robotic solutions (Zhang et al., 2022). By integrating soft robotic grippers into a hard robotic frame and incorporating switchable and frequency-driven micro-patterned adhesives, it is possible to create a system that can effectively grasp, pick and place, and manipulate satellite debris on demand in the harsh conditions of outer space (Kwak et al., 2011; Dayan et al., 2021; Barnefske et al., 2022). However, the overall cost effectiveness remains an open question that can be solved by scalable fabrication techniques and repurposing (Li S. et al., 2021; Kim et al., 2023).

Embodied and physical intelligence plays a crucial role in developing autonomous soft robots that can effectively acquire, analyze, and utilize large amounts of data to tackle complex tasks such as navigating difficult trajectories, changing shape, and manipulating unknown and unstructured objects in various environments (Fischer et al., 2023). EI can simplify control parameters, resulting in more energy-efficient and cost-effective operations, but it requires a careful mechanical and material design to consider the interaction with the environment while performing a task (Laschi, 2022). Soft robotic systems with EI can benefit from incorporating AI-learning architecture and computational modeling to overcome the challenges posed by the complex dynamic interactions and body deformations that occur in open-world tasks (George Thuruthel et al., 2018; Thuruthel et al., 2019; Shih et al., 2020).

For instance, a group of self-sufficient and field-ready soft robots can be designed in their constitutive materials and mechanical structures specifically for exploring and restoring archaeological sites (SDG-15). These tasks require precision and safe manipulation, as well as an interaction with hard-to-reach environments and unstructured cracks. Thus, the authors envision a group of self-piloting soft robots with EI and efficient computational architecture. Each individual robot can work with the other like the “wood wide web” between trees and other organisms, to face hazardous conditions, minimize damage during retrieval, and ultimately be more cost-effective (Mazzolai et al., 2022). Although current solutions for this type of architectures are still energetically costly and in their early stages, effective solutions are expected to be developed in the future to help tackle some of humanity’s grand challenges in support of some of the SDGs and climate crisis.