This work presents an overview of the techni-cal details behind a high-performance reinforcement learning policy deployment with the Spot RL Researcher Development Kit for low-level motor access on Boston Dynamic's Spot. This represents the first public demonstration of an end-to-end reinforcement learning policy deployed on Spot hardware with training code publicly available through Nvidia IsaacLab and deployment code available through Boston Dynamics. We utilize Wasserstein Distance and Maximum Mean Discrepancy to quantify the distributional dissimilarity of data collected on hardware and in simulation to measure our sim-to-real gap. We use these measures as a scoring function for the Covariance Matrix Adaptation Evolution Strategy to optimize simulated parameters that are unknown or difficult to measure from Spot. Our procedure for modeling and training produces high-quality reinforcement learning policies capable of multiple gaits, including a flight phase. We deploy policies capable of over 5.2m/s locomotion, more than triple Spot's default controller maximum speed, robustness to slippery surfaces, disturbance rejection, and overall agility previously unseen on Spot. We detail our method and release our code to support future work on Spot with the low-level API.

We present Diffuse-CLoC, a guided diffusion framework for physics-based look-ahead control that enables intuitive, steerable, and physically realistic motion generation. While existing kinematics motion generation with diffusion models offer intuitive steering capabilities with inference-time conditioning, they often fail to produce physically viable motions. In contrast, recent diffusion-based control policies have shown promise in generating physically realizable motion sequences, but the lack of kinematics prediction limits their steerability. Diffuse-CLoC addresses these challenges through a key insight: modeling the joint distribution of states and actions within a single diffusion model makes action generation steerable by conditioning it on the predicted states. This approach allows us to leverage established conditioning techniques from kinematic motion generation while producing physically realistic motions. As a result, we achieve planning capabilities without the need for a high-level planner. Our method handles a diverse set of unseen long-horizon downstream tasks through a single pre-trained model, including static and dynamic obstacle avoidance, motion in-betweening, and task-space control. Experimental results show that our method significantly outperforms the traditional hierarchical framework of high-level motion diffusion and low-level tracking.

Intelligent reflecting surface (IRS) is considered as an enabling technology for millimeter wave (mmWave) communications since it can provide an extra reflection link to increase the macrodiversity gains and improve the transmission performance. These advantages principally rely on the assumption that the blocking of direct and reflection links is mutually independent. However, due to the non-negligible sizes of blockages in some practical cases, both links may be simultaneously blocked by the same blockage, which is the so-calledblockage correlation. To this end, in this work we investigate the impact of blockage correlation in the IRS-assisted mmWave communication systems. Firstly, we provide a new joint line-of-sight/non-line-of-sight (LOS/NLOS) probability model by considering blockage correlation. The correlation coefficient of direct and reflection link states is also derived. Secondly, we study the effects of blockage correlation on the system performance of IRS-assisted mmWave communication systems by deriving the expression of transmission success probability. Moreover, we provide a deployment optimization algorithm for IRS when blockage correlation is considered. Finally, simulations verify our theoretical results and validate the IRS deployment optimization algorithm. It is shown that the widely-used independent blocking assumption not only always incur an overestimation of the performance of IRS-assisted mmWave communication systems, but also misdirect the optimal deployment of IRS.

Compared to reflective-only or transmissive-only reconfigurable intelligent surface (RIS), simultaneous transmitting and reflecting RIS (STAR-RIS) has the capability to simultaneously serve users on its both sides. Then, simultaneous indoor-and-outdoor communication (SIOC) has been commonly regarded as one of the most evident and promising applications of STAR-RIS in the next generation wireless networks. However, it introduces intricate interference for wireless networks, i.e., transmission and reflection interference, which would impact the network coverage and transmission capacity drastically. Unfortunately, there is a dearth of research investigating STAR-RIS-enabled SIOC from a network-level perspective. To fill the gap, by leveraging stochastic geometry, we provide the first comprehensive analytical framework for STAR-RIS-enabled SIOC networks. We investigate three commonly used STAR-RIS protocols, i.e., energy splitting (ES), time switching (TS) and mode switching (MS), and consider both unicast communication (UC) and multicast communication (MC) schemes. Exact and approximate expressions of transmission success probability and transmission capacity are derived. Furthermore, we also optimize the key parameters of STAR-RIS protocols to maximize the transmission capacity. Finally, via simulations, the accuracy of theoretical expressions is confirmed, the optimization of the key parameters is investigated, and the advantages of STAR-RIS over conventional RISs are also demonstrated. Moreover, we also conclude the recommended STAR-RIS protocols to UC and MC schemes, respectively.

In this paper, we present a unified performance analysis of stochastic clustered cooperative systems with distance-based relay selection, where the destination is located at the cluster center, the locations of the source and the candidate relays follow an independent and identical distribution, and there exists a Poisson field of interferers. As the distance-based relay selection leads to spatial correlation between cooperative transmission distances in a cluster, we first derive a general joint probability distribution function of cooperative transmission distances based on$\psi $-order statistics. Then, the transmission success probability of the stochastic clustered cooperative systems with decode-and-forward (DF) scheme is derived. In order to reduce computational complexity, we offer an approximate expression of the transmission success probability. Furthermore, we provide an asymptotic transmission success probability in the interference-limited scenario, when the number of candidate relays in a cluster is sufficiently large. Besides, the performance analysis framework is extended into the clustered cooperative systems with finite relay distribution regions. Taking the cluster member distributions of Thomas cluster process (TCP) and Matérn cluster process (MCP) as examples, we verify our theoretical analysis via Monte-Carlo simulations. With the theoretical results, the optimization of relay selection parameter is also demonstrated.

We consider a wireless-powered ad hoc network with clustered Power Beacons (PBs). The wireless-powered transmitters (WP-Txs) only activate stochastically deployed PBs within the circles centered at their locations. The PBs transmit radio frequency (RF) signals, which are harvested by the WP-Txs to transmit information to their target receivers. The nodes are modeled as following$\beta $-Ginibre Point Processes ($\beta $-GPP), which incorporate repulsion among PBs or active WP-Txs. Lacking analytical mathematical tools to describe the distribution of clustered PBs, we establish lower and upper bounds on the energy outage probability, as well as an approximated expression of it. We then derive the information outage probability, an approximate expression of it by approximating the reduced Palm distribution of$\beta $-GPP as an inhomogeneous Poisson point process (IPPP), and an analytical approximate expression of it by further ignoring the small-scale fading of interfering WP-Tx links. Finally, the transmission capacity is obtained based on the overall outage probability. The simulation results show that the derived theoretical expressions accurately reflect the performance of the wireless-powered ad hoc networks and the performance gap introduced by approximations is negligible. Transmission capacity optimization based on the derived expressions is also possible and demonstrated.

In this paper, we analyze the energy coverage performance of heterogeneous millimeter wave (mmWave) wireless power transfer (WPT) networks, where macro base stations (MBSs) are distributed according to a Poisson point process (PPP), the location of power beacons (PBs) is modeled as a k-tier Poisson cluster process (PCP), and energy users (EUs) are clustered around the centers of PB clusters. Moreover, the cosine antenna gain model is adopted instead of the prevalent flap-top gain model, which is simpler in derivation but less accurate. Based on the generalized exponential distribution approximation, we propose a new technique of deriving the energy coverage probability of randomly deployed mmWave WPT networks. Specifically, taking the Thomas cluster process (TCP) for instance, we derive the energy coverage probabilities with two PB association strategies, i.e., the random PB association and the nearest PB association. Through Monte-Carlo simulations, our theoretical results are verified and the impact of system parameters, such as the array antenna size, energy threshold or average number of PBs in a cluster, are also investigated.