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-ofpractice in enabling legged robotic systems to accomplish realworld 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. 3 The proposed solution contributed to winning 1st-place in the 2020 DARPA Subterranean Challenge, Urban Circuit. 4

This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge. Specifically, it presents the techniques utilized within the Tunnel (2019) and Urban (2020) competitions, where CoSTAR achieved 2nd and 1st place, respectively. We also discuss CoSTAR's demonstrations in Martian-analog surface and subsurface (lava tubes) exploration. The paper introduces our autonomy solution, referred to as NeBula (Networked Belief-aware Perceptual Autonomy). NeBula is an uncertainty-aware framework that aims at enabling resilient and modular autonomy solutions by performing reasoning and decision making in the belief space (space of probability distributions over the robot and world states). We discuss various components of the NeBula framework, including: (i) geometric and semantic environment mapping; (ii) a multi-modal positioning system; (iii) traversability analysis and local planning; (iv) global motion planning and exploration behavior; (i) risk-aware mission planning; (vi) networking and decentralized reasoning; and (vii) learning-enabled adaptation. We discuss the performance of NeBula on several robot types (e.g. wheeled, legged, flying), in various environments. We discuss the specific results and lessons learned from fielding this solution in the challenging courses of the DARPA Subterranean Challenge competition.

Simultaneous localization and Planning (SLAP) is a crucial ability for an autonomous robot operating under uncertainty. In its most general form, SLAP induces a continuous POMDP (partially-observable Markov decision process), which needs to be repeatedly solved online. This paper addresses this problem and proposes a dynamic replanning scheme in belief space. The underlying POMDP, which is continuous in state, action, and observation space, is approximated offline via sampling-based methods, but operates in a replanning loop online to admit local improvements to the coarse offline policy. This construct enables the proposed method to combat changing environments and large localization errors, even when the change alters the homotopy class of the optimal trajectory. It further outperforms the state-of-the-art FIRM (Feedback-based Information RoadMap) method by eliminating unnecessary stabilization steps. Applying belief space planning to physical systems brings with it a plethora of challenges. A key focus of this paper is to implement the proposed planner on a physical robot and show the SLAP solution performance under uncertainty, in changing environments and in the presence of large disturbances, such as a kidnapped robot situation.

Decision-making under uncertainty is a crucial ability for autonomous systems. In its most general form, this problem can be formulated as a Partially Observable Markov Decision Process (POMDP). The solution policy of a POMDP can be implicitly encoded as a value function. In partially observable settings, the value function is typically learned via forward simulation of the system evolution. Focusing on accurate and long-range risk assessment, we propose a novel method, where the value function is learned in different phases via a bi-directional search in belief space. A backward value learning process provides a long-range and risk-aware base policy. A forward value learning process ensures local optimality and updates the policy via forward simulations. We consider a class of scalable and continuous-space rover navigation problems (RNP) to assess the safety, scalability, and optimality of the proposed algorithm. The results demonstrate the capabilities of the proposed algorithm in evaluating long-range risk/safety of the planner while addressing continuous problems with long planning horizons.Proof. We prove this by backward induction.Consider the terminal beliefs first. Trivially, from Eq. (4) and Eq. (8), ρ(b g ; π) = lim J F →∞ J(bg;π) J F = 0 for ∀b g ∈ B goal , and ρ(b f ; π) = lim J F →∞

Objective: To compare in vivo and in vitro mechanical stability of orthodontic mini-implants (OMIs) treated with a sandblasted, large-grit, and anodic-oxidation (SLAO) method vs those treated with a sandblasted, large-grit, and acid-etching (SLA) method. Materials and Methods: Fifty-four titanium OMIs (cylindrical shape, drill-free type; diameter 5 1.45 mm, length 5 8 mm, Biomaterials Korea Inc, Seoul, Korea) were allocated into control, SLA, and SLAO groups (N 5 12 for in vivo and N 5 6 for in vitro studies per group). In vitro study was carried out on a polyurethane foam bone block (Sawbones, Pacific Research Laboratories Inc, Vashon, Wash). In vivo study was performed in the tibias of Beagles (6 males, age 5 1 year, weight 5 10 to 13 kg; OMIs were removed at 8 weeks after installation). For insertion and removal of OMIs, the speed and maximum torque of the surgical engine were set to 30 rpm and 40 Ncm, respectively. Maximum torque (MT), total energy (TE), and near peak energy (NPE) during the insertion and removal procedures were statistically analyzed. Results: In the in vitro study, although the control group had a higher insertion MT value than the SLA and SLAO groups (P , .01), no differences in insertion TE and NPE or in any of the removal variables were noted among the three groups. In the in vivo study, the control group exhibited higher values for all insertion variables compared with the SLA and SLAO groups (MT, P , .001; TE, P , .01; NPE, P , .001). Although no difference in removal TE and removal NPE was noted among the three groups, the SLAO group presented with a higher removal MT than the SLA and control groups (P , .001). Conclusions: SLAO treatment may be an effective tool in reducing insertion damage to surrounding tissue and improving the mechanical stability of OMIs. (Angle Orthod. 2012;82: 611-617.)