We propose a unified strategy for fast goal inference in human–robot interaction. The core idea is to drive the human toward Critical Decision Points (CDPs)–states where competing human strategies prescribe different next actions and thus maximally reveal the goal. We formalise CDPs using a goal-conditioned policy divergence measure and incorporate them into a Receding-Horizon Planner that explores future action sequences while optimizing a cost function balancing task progress and information gain. We evaluate this approach in both a collaborative, fully observable cooking task and a competitive, partially observable hide-and-seek game, each in simulation and on real robots. In both scenarios, our method infers human goals more accurately and earlier than baseline strategies.
Unmanned aerial vehicle (UAV) swarm agents operating in radio-frequency (RF)-degraded environments require coordination mechanisms that connect directly observable signals to executable responses. This work presents an embodied visual-communication approach in which bio-inspired motion–LED glyphs are represented by a reduced six-parameter semantic chart embedded within a full 24-dimensional hybrid execution manifold. A learned translator large language model (LLM) maps a perceived glyph to a response that is instantiated as an executable trajectory and propagated through closed-loop quadrotor dynamics. The system is evaluated in 200 three-UAV search-and-rescue trials with receiver-specific degradation from sensing range, field of view, occlusion, and relative motion. Under clean observations, the quantized small model and rule-based translator both achieve 100% semantic correctness, but under single- and two-parameter corruption, the quantized small model achieves 64.7% and 64.1% , compared with 44.8% and 35.2% for rule-based, while reducing mean multi-agent trajectory error from 2.902 m to 0.993 m . All methods maintain 100% rotor-allocation and finite-horizon feasibility. The quantized small model also has comparable overall semantic correctness with the large model ( 83.3% vs. 86.3% ) while dropping latency from 5.115 s to 2.789 s and is validated onboard a Jetson–Pixhawk UAV platform, where airborne inference and bounded command execution produce measurable physical motion. These results establish a unified pathway from degraded visual observation to semantically meaningful, dynamically grounded UAV coordination.
In swarm robotics, stigmergy (indirect communication through changes to the environment) offers an alternative to direct communication and centralised control when these are unavailable. We investigate whether stigmergy alone can solve a group-formation task, namely controlling the number of robots that gather into a group. To this end, a leader robot lays a phosphorescent trail by exciting the arena surface with ultraviolet light; the brightness of the trail decays over time, and its initial intensity encodes the target group size. Following robots join the trail and leave it once the measured brightness falls below a threshold, so that the group size is regulated without any direct inter-robot communication. We demonstrate this mechanism as a proof of concept on real hardware, using a swarm of small Dezibot robots, and show that groups of a specified size form and are maintained over an extended period of time.
Robotic assembly in semi-structured environments represents a critical challenge in flexible manufacturing. While existing hybrid methods benefit from knowledge-based hierarchies, model-based planning, and data-driven learning, they still struggle with small-batch, precise assembly tasks due to loose coupling between components and high training costs. To address these limitations, we introduce Cognition-inspired Robot Learning and Manipulation (CRLM), a neuro-symbolic framework that tightly couples structured task knowledge, semi-supervised perception, and residual reinforcement learning for efficient skill acquisition and robust execution. Inspired by cognitive apprenticeship theory, CRLM operationalizes three developmental phases—modeling, scaffolding, and fading—through a hierarchical manipulation architecture. Specifically, explicit symbolic stage graphs and Object-Embodiment-Centric (OEC) waypoints provide interpretable task decomposition and safety bounds; semi-supervised visual learning bootstraps global and local object detectors from sparse embodied data; and a residual fine policy trained via structured-to-semi (STS) transfer learns contact-aware corrections from multimodal visual and force feedback. By integrating these components into a coherent developmental pipeline rather than treating them as isolated sequential blocks, CRLM achieves robust contact-rich assembly with minimal human engineering. Simulation experiments demonstrate substantial improvements of 13
