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About

Thomas Eiband is a researcher whose work spans two compelling domains: assistive robotics and human-robot interaction, with a particular focus on robot programming, skill learning, and prosthetic control. His early contributions explored myocontrol for prosthetic devices, investigating how combining force sensing with electromyography could overcome longstanding limitations in dexterous prosthesis control. This work, now cited 24 times, reflects his enduring interest in bridging human intention and robotic action. The bulk of Eiband's research addresses intuitive robot programming through Programming by Demonstration (PbD), enabling non-expert users to teach robots complex tasks without traditional coding. He has made notable advances in task segmentation, haptic exploration, conditional task learning, and recovery behavior programming—contributions that collectively push collaborative robotics closer to real-world industrial deployment. His survey on capability-based frameworks for robot skills (13 citations) further demonstrates his commitment to standardizing how robot capabilities are represented and communicated across systems. More recently, Eiband has tackled practical challenges in robotic surface finishing and ontological task representation, broadening his impact into manufacturing automation. With papers accumulating citations across multiple research communities, his work stands as a meaningful bridge between human-centered design and autonomous robotic systems, making him a valuable voice in modern robotics research.