These point toward a perception‐native electronic skin, whose stimulus‐to‐perception pathway is built into the skin, not a remote processor—a foundation for humanoids that act by touch, not just sight.
Abstract
Humanoid robots enter human environments, where manipulating objects with dexterity, keeping balance while walking, and staying safe under unexpected contact depend on touch; unlike vision, touch must cover the whole body. Individual sensors already rival human sensitivity, but no single design fits every region: each needs its own mix of density, sensitivity, and modality, but no whole‐body skin yet integrates them. This review traces the problem across the device, system, and intelligence levels, and finds the obstacle to be less the device than the system. Dense tactile streams are read out, relayed, and closed into control loops, but interconnect proliferation and von Neumann data movement keep them from closing in milliseconds. Array‐level fixes relieve the wiring burden only by shifting it onto computation, leaving a trade‐off between how richly a region can sense and how fast it can act. Resolving this without sacrificing latency demands bio‐inspired sensor–computing integration—near‐sensor, in‐sensor, and neuromorphic schemes emulating the body's receptors and the synapses and neurons relaying their signals, concentrating computation where it is needed. These point toward a perception‐native electronic skin, whose stimulus‐to‐perception pathway is built into the skin, not a remote processor—a foundation for humanoids that act by touch, not just sight.
Robots operating in close proximity to humans must be capable of perceiving contact, reacting within milliseconds, and subsequently restoring stable task execution. However, existing proprioceptive or vision‐based methods suffer from limited bandwidth, high latency, and poor reliability during dynamic interactions. H...
Jinhyeok Oh, Hun Jang, Ju-Sung Choi et al.· Advanced Functional Material...· 0 citations
This article showcases the architectural bottlenecks, material science challenges, and embedded computing requirements that influence whether E-skin can transition from laboratory prototypes to deployable robotic systems, and claims that the next E-skin trends will be built on top of heterogeneous architectures compose...
Ioannis Papadongonas, Alexandros Gazis, Vasileios Fanidis et al.· Academia Engineering· 0 citations
A growing body of work suggests that tactile sensing gives robot policies contact information that complements vision in dexterous manipulation. However, visuo-tactile robot data remains scarce: dexterous demonstrations require teleoperating robots, which limits dataset scale. Human demonstrations are far cheaper to co...
Jing-Yun Yang, Bai-Yu Shi, Timothy Yu et al.· 0 citations
Humans manipulate objects within their hands through touch alone with remarkable ease, but for robots, achieving sustained real-world manipulation without vision remains an open challenge. Although reinforcement learning (RL) can generate sophisticated blind manipulation policies in simulation, transferring them to a r...
A. Deshmukh, Nalin Agarwal, Elle Miller et al.· 0 citations
Neuromorphic devices with learning and memory functions are critical for enabling human‐like tactile perception and interactive control in humanoid robots. However, existing neuromorphic platforms often struggle to reconcile mechanical flexibility with energy efficiency due to material and mechanism constraints. Here...
Xin-Yu Li, Kang Chen, Xiang-Ling Xia et al.· FlexMat· 0 citations
Humanoid robots have many degrees of freedom and contact surfaces, yet most loco-manipulation research restricts manipulation to the hands and avoids other body contacts with the environment. To extend manipulation beyond the hands, we present PanTact, a system that learns humanoid whole-body dexterity, the capability...
Xiao-Meng Xu, Han Zhang, Zhian Ruan et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.