Unknown dynamic load carrying is one important practical application for quadruped robots. Such a problem is non-trivial, posing three major challenges in quadruped locomotion control. First, how to model or represent the dynamics of the load in a generic manner. Second, how to make the robot capture the dynamics without any external sensing. Third, how to enable the robot to interact with load handling the mutual effect and stabilizing the load. In this work, we propose a general load modeling approach called load characteristics modeling to capture the dynamics of the load. We integrate this proposed modeling technique and leverage recent advances in Reinforcement Learning (RL) based locomotion control to enable the robot to infer the dynamics of load movement and interact with the load indirectly to stabilize it and realize the sim-to-real deployment to verify its effectiveness in real scenarios. We conduct extensive comparative simulation experiments to validate the effectiveness and superiority of our proposed method. Results show that our method outperforms other methods in sudden load resistance, load stabilizing and locomotion with heavy load on rough terrain. \href{https://leixinjonaschang.github.io/leggedloadadapt.github.io/}{Project Page}.

Motivated by applications in job scheduling, queuing networks, and load balancing in cyber-physical systems, we develop and analyze a game-theoretic framework to balance the load among servers in both static and dynamic settings. In these applications, jobs/tasks are often held by selfish entities that do not want to coordinate with each other, yet the goal is to balance the load among servers in a distributed manner. First, we provide a static game formulation in which each player holds a job with a certain processing requirement and wants to schedule it fractionally among a set of heterogeneous servers to minimize its average processing time. We show that this static game is a potential game and admits a pure Nash equilibrium (NE). In particular, the best-response dynamics converge to such an NE after $n$ iterations, where $n$ is the number of players. We then extend our results to a dynamic game setting, where jobs arrive and get processed in the system, and players observe the load (state) on the servers to decide how to schedule their jobs among the servers in order to minimize their averaged cumulative processing time. In this setting, we show that if the players update their strategies using dynamic best-response strategies, the system eventually becomes fully load-balanced and the players' strategies converge to the pure NE of the static game. In particular, we show that the convergence time scales only polynomially with respect to the game parameters. Finally, we provide numerical results to evaluate the performance of our proposed algorithms under both static and dynamic settings.

We investigate GPU-based parallelization of Iterative-Deepening A* (IDA*). We show that straightforward thread-based parallelization techniques which were previously proposed for massively parallel SIMD processors perform poorly due to warp divergence and load imbalance. We propose Block-Parallel IDA* (BPIDA*), which assigns the search of a subtree to a block (a group of threads with access to fast shared memory) rather than a thread. On the 15-puzzle, BP-IDA* on a NVIDIA GRID K520 with 1536 CUDA cores achieves a speedup of 4.98 compared to a highly optimized sequential IDA* implementation on a Xeon E5-2670 core.