This paper addresses the problem of task planning for robots that must comply with operational manuals in real-world settings. Task planning under these constraints is essential for enabling autonomous robot operation in domains that require adherence to domain-specific knowledge. Current methods for generating robot goals and plans rely on common sense knowledge encoded in large language models. However, these models lack grounding of robot plans to domain-specific knowledge and are not easily transferable between multiple sites or customers with different compliance needs. In this work, we present SayComply, which enables grounding robotic task planning with operational compliance using retrievalbased language models. We design a hierarchical database of operational, environment, and robot embodiment manuals and procedures to enable efficient retrieval of the relevant context under the limited context length of the LLMs. We then design a task planner using a tree-based retrieval augmented generation (RAG) technique to generate robot tasks that follow user instructions while simultaneously complying with the domain knowledge in the database. We demonstrate the benefits of our approach through simulations and hardware experiments in real-world scenarios that require precise context retrieval across various types of context, outperforming the standard RAG method. Our approach bridges the gap in deploying robots that consistently adhere to operational protocols, offering a scalable and edge-deployable solution for ensuring compliance across varied and complex real-world environments. Project website: saycomply.github.io.

This paper addresses the problem of autonomous robotic inspection in complex and unknown environments. This capability is crucial for efficient and precise inspections in various real-world scenarios, even when faced with perceptual uncertainty and lack of prior knowledge of the environment. Existing methods for real-world autonomous inspections typically rely on predefined targets and waypoints and often fail to adapt to dynamic or unknown settings. In this paper, we introduce the Semantic Belief Behavior Graph (SB2G) framework as a new approach to semantic-aware autonomous robot inspection. SB2G generates a control policy for the robot, using behavior nodes that encapsulate various semantic-based policies designed for inspecting different classes of objects. We design an active semantic search behavior to guide the robot in locating objects for inspection while reducing semantic information uncertainty. The edges in the SB2G encode transitions between these behaviors. We validate our approach through simulation and real-world urban inspections using a legged robotic platform. Our results show that SB2G enables a more efficient object inspection policy, exhibiting similar behaviors comparable to human-operated inspections.

To achieve autonomy in unknown and unstruc-tured environments, we propose a method for semantic-based planning under perceptual uncertainty. This capability is cru-cial for safe and efficient robot navigation in environment with mobility-stressing elements that require terrain-specific locomotion policies. We propose the Semantic Belief Graph (SBG), a geometric- and semantic-based representation of a robot's probabilistic roadmap in the environment. The SBG nodes comprise of the robot geometric state and the semantic-knowledge of the terrains in the environment. The SBG edges represent local semantic-based controllers that drive the robot between the nodes or invoke an information gathering action to reduce semantic belief uncertainty. We formulate a semantic-based planning problem on SBG that produces a policy for the robot to safely navigate to the target location with min-imal traversal time. We analyze our method in simulation and present real-world results with a legged robotic platform navigating multi-level outdoor environments.

To achieve autonomy in complex real-world exploration missions, we consider deployment strategies for a team of robots with heterogeneous capabilities. We formulate a multi-robot exploration mission and compute an operation policy to maintain robot team productivity and maximize mission success. The environment description, robot capability, and mission outcome are modeled as a Markov decision process (MDP). We also include constraints, such as sensor failures, limited communication coverage, and mobility-stressing elements. The proposed operation model is applied to the DARPA Subterranean (SubT) Challenge. The deployment policy is also compared against the human-based operation strategy in the final competition of the SubT Challenge.

This paper serves as one of the first efforts to enable large-scale and long-duration autonomy using the Boston Dynamics Spot robot. Motivated by exploring extreme environments, particularly those involved in the DARPA Subterranean Challenge, this paper pushes the boundaries of the state-of-practice in enabling legged robotic systems to accomplish real-world complex missions in relevant scenarios. In particular, we discuss the behaviors and capabilities which emerge from the integration of the autonomy architecture NeBula (Networked Belief-aware Perceptual Autonomy) with next-generation mobility systems. We will discuss the hardware and software challenges, and solutions in mobility, perception, autonomy, and very briefly, wireless networking, as well as lessons learned and future directions. We demonstrate the performance of the proposed solutions on physical systems in real-world scenarios.3The proposed solution contributed to winning 1st-place in the 2020 DARPA Subterranean Challenge, Urban Circuit.4