arXiv subjects

Lixiao Huang

Publications and source records attributed to Lixiao Huang.

3 recordsLinked to original sources

arXiv2026

Humanoid Factors: Design Principles for AI Humanoids in Human Worlds

Human factors research has long focused on optimizing environments, tools, and systems to account for human performance. Yet, as humanoid robots begin to share our workplaces, homes, and public spaces, the design challenge expands. We must now consider not only factors for humans but also factors for humanoids, since both will coexist and interact within the same environments. Unlike conventional machines, humanoids introduce expectations of human-like behavior, communication, and social presence, which reshape usability, trust, and safety considerations. In this article, we introduce the concept of humanoid factors as a framework structured around four pillars - physical, cognitive, social, and ethical - that shape the development of humanoids to help them effectively coexist and collaborate with humans. This framework characterizes the overlap and divergence between human capabilities and those of general-purpose humanoids powered by AI foundation models. To demonstrate our framework's practical utility, we then apply the framework to evaluate a real-world humanoid control algorithm, illustrating how conventional task completion metrics in robotics overlook key human cognitive and interaction principles. We thus position humanoid factors as a foundational framework for designing, evaluating, and governing sustained human-humanoid coexistence.

cs.RO↗

arXiv2026

Continual Robot Skill and Task Learning via Dialogue

Interactive robot learning is a challenging problem as the robot is present with human users who expect the robot to learn novel skills to solve novel tasks perpetually with sample efficiency. In this work we present a framework for robots to continually learn tasks and visuo-motor skills and query for novel skills via dialog interactions with human users. Our robot agent maintains a skill library, and uses an existing LLM to perform grounded dialog interactions to query unknown skills from real human users. We developed a novel visual-motor control policy Action Chunking Transformer with Low Rank Adaptation (ACT-LoRA) that can continually learn novel skills using only a few demonstrations which is critical in human-robot interaction scenarios. The paper has twin goals: Firstly to demonstrate better continual learning in simulation; and secondly, to demonstrate the use of our dialog based learning framework in a realistic human-robot interaction use case. Our ACT-LoRA policy consistently outperforms a GMM-LoRA baseline on multiple continual learning simulation benchmarks by achieving > 300% improvements on novel skills, while achieving comparable performance in existing skills. Moreover, with our IRB approved human-subjects study we demonstrate that our dialog based continual learning framework allows users to teach robots cooking skills successfully (100%) while spending a higher ratio of time on finishing an auxiliary distraction tasks in the test phase of the study compared to a non-learning language based agent (p < 0.001).

cs.RO↗

arXiv2023

Improving the State of the Art for Training Human-AI Teams: Technical Report #3 -- Analysis of Testbed Alternatives

Sonalysts is working on an initiative to expand our current expertise in teaming to Human-Artificial Intelligence (AI) teams by developing original research in this area. To provide a foundation for that research, Sonalysts is investigating the development of a Synthetic Task Environment (STE). In a previous report, we documented the findings of a recent outreach effort in which we asked military Subject Matter Experts (SMEs) and other researchers in the Human-AI teaming domain to identify the qualities that they most valued in a testbed. A surprising finding from that outreach was that several respondents recommended that our team look into existing human-AI teaming testbeds, rather than creating something new. Based on that recommendation, we conducted a systematic investigation of the associated landscape. In this report, we describe the results of that investigation. Building on the survey results, we developed testbed evaluation criteria, identified potential testbeds, and conducted qualitative and quantitative evaluations of candidate testbeds. The evaluation process led to five candidate testbeds for the research team to consider. In the coming months, we will assess the viability of the various alternatives and begin to execute our program of research.

cs.HC↗