City-scale traffic signal control (TSC) involves thousands of heterogeneous intersections with varying topologies, making cooperative decision-making across intersections particularly challenging. Given the prohibitive computational cost of learning individual policies for each intersection, some researchers explore learning a universal policy to control each intersection in a decentralized manner, where the key challenge is to construct a universal representation method for heterogeneous intersections. However, existing methods are limited to universally representing information of heterogeneous ego intersections, neglecting the essential representation of influence from their heterogeneous neighbors. Universally incorporating neighborhood information is nontrivial due to the intrinsic complexity of traffic flow interactions, as well as the challenge of modeling collective influences from neighbor intersections. To address these challenges, we propose CityLight, which learns a universal policy based on representations obtained with two major modules: a Neighbor Influence Encoder to explicitly model neighbor's influence with specified traffic flow relation and connectivity to the ego intersection; a Neighbor Influence Aggregator to attentively aggregate the influence of neighbors based on their mutual competitive relations. Extensive experiments on five city-scale datasets, ranging from 97 to 13,952 intersections, confirm the efficacy of CityLight, with an average throughput improvement of 11.68% and a lift of 22.59% for generalization. Our codes and datasets are released: https://github.com/tsinghua-fib-lab/CityLight.

Vision-Language-Action (VLA) models have made substantial progress by leveraging the robust capabilities of Visual Language Models (VLMs).However, VLMs' significant parameter size and autoregressive (AR) decoding nature impose considerable computational demands on VLA models.While Speculative Decoding (SD) has shown efficacy in accelerating Large Language Models (LLMs) by incorporating efficient drafting and parallel verification, allowing multiple tokens to be generated in one forward pass, its application to VLA models remains unexplored.This work introduces Spec-VLA, an SD framework designed to accelerate VLA models.Due to the difficulty of the action prediction task and the greedy decoding mechanism of the VLA models, the direct application of the advanced SD framework to the VLA prediction task yields a minor speed improvement.To boost the generation speed, we propose an effective mechanism to relax acceptance utilizing the relative distances represented by the action tokens of the VLA model.Empirical results across diverse test scenarios affirm the effectiveness of the Spec-VLA framework, and further analysis substantiates the impact of our proposed strategies, which enhance the acceptance length by 44%, achieving 1.42× speedup compared with the OpenVLA baseline, without compromising the success rate.The success of the Spec-VLA framework highlights the potential for broader application of speculative execution in VLA prediction scenarios.We make our code and data publicly available at https: //github.com/PineTreeWss/SpecVLA.

Reinforcement learning (RL) for continuous control often requires large amounts of online interaction data. Value-based RL methods can mitigate this burden by offering relatively high sample efficiency. Some studies further enhance sample efficiency by incorporating offline demonstration data to “kick-start” training, achieving promising results in continuous control. However, they typically compute the Q-function independently for each action dimension, neglecting interdependencies and making it harder to identify optimal actions when learning from suboptimal data, such as non-expert demonstration and online-collected data during the training process. To address these issues, we propose Auto-Regressive Soft Q-learning (ARSQ), a value-based RL algorithm that models Q-values in a coarse-to-fine, auto-regressive manner. First, ARSQ decomposes the continuous action space into discrete spaces in a coarse-to-fine hierarchy, enhancing sample efficiency for fine-grained continuous control tasks. Next, it auto-regressively predicts dimensional action advantages within each decision step, enabling more effective decision-making in continuous control tasks. We evaluate ARSQ on two continuous control benchmarks, RLBench and D4RL, integrating demonstration data into online training. On D4RL, which includes non-expert demonstrations, ARSQ achieves an average 1.62$\times$ performance improvement over SOTA value-based baseline. On RLBench, which incorporates expert demonstrations, ARSQ surpasses various baselines, demonstrating its effectiveness in learning from suboptimal online-collected data.

Large language model (LLM) agents have recently demonstrated impressive capabilities in various domains like open-ended conversation and multi-step decision-making. However, it remains challenging for these agents to solve strategic language games, such as Werewolf, which demand both strategic decision-making and free-form language interactions. Existing LLM agents often suffer from intrinsic bias in their action distributions and limited exploration of the unbounded text action space, resulting in suboptimal performance. To address these challenges, we propose Latent Space Policy Optimization (LSPO), an iterative framework that combines game-theoretic methods with LLM fine-tuning to build strategic language agents. LSPO leverages the observation that while the language space is combinatorially large, the underlying strategy space is relatively compact. We first map free-form utterances into a finite latent strategy space, yielding an abstracted extensive-form game. Then we apply game-theoretic methods like Counterfactual Regret Minimization (CFR) to optimize the policy in the latent space. Finally, we fine-tune the LLM via Direct Preference Optimization (DPO) to align with the learned policy. By iteratively alternating between these steps, our LSPO agents progressively enhance both strategic reasoning and language communication. Experiment on the Werewolf game shows that our agents iteratively expand the strategy space with improving performance and outperform existing Werewolf agents, underscoring their effectiveness in free-form language games with strategic interactions.

Navigating in human-filled public spaces is a critical challenge for deploying autonomous robots in real-world environments. This paper introduces NaviDIFF, a novel Hamiltonian-constrained socially-aware navigation framework designed to address the complexities of human-robot interaction and socially-aware path planning. NaviDIFF integrates a port-Hamiltonian framework to model dynamic physical interactions and a diffusion model to manage uncertainty in human-robot cooperation. The framework leverages a spatial-temporal transformer to capture social and temporal dependencies, enabling more accurate spatial-temporal environmental dynamics understanding and port-Hamiltonian physical interactive process construction. Additionally, reinforcement learning from human feedback is employed to fine-tune robot policies, ensuring adaptation to human preferences and social norms. Extensive experiments demonstrate that NaviDIFF outperforms state-of-the-art methods in social navigation tasks, offering improved stability, efficiency, and adaptability11The experimental videos and additional information about this work can be found at: https://sites.google.com/view/NaviDIFF.

We present a large language model (LLM) based system to empower quadrupedal robots with problem-solving abilities for long-horizon tasks beyond short-term motions. Long-horizon tasks for quadrupeds are challenging since they require both a high-level understanding of the semantics of the problem for task planning and a broad range of locomotion and manipulation skills to interact with the environment. Our system builds a high-level reasoning layer with large language models, which generates hybrid discrete-continuous plans as robot code from task descriptions. It comprises multiple LLM agents: a semantic planner that sketches a plan, a parameter calculator that predicts arguments in the plan, a code generator that converts the plan into executable robot code, and a replanner that handles execution failures or human interventions. At the low level, we adopt reinforcement learning to train a set of motion planning and control skills to unleash the flexibility of quadrupeds for rich environment interactions. Our system is tested on long-horizon tasks that are infeasible to complete with one single skill. Simulation and real-world experiments show that it successfully figures out multi-step strategies and demonstrates non-trivial behaviors, including building tools or notifying a human for help. Demos are available on our project page: https://sites.google.com/view/long-horizon-robot.

This paper tackles the challenging task of maintaining formation among multiple unmanned aerial vehicles (UAVs) while avoiding both static and dynamic obstacles during directed flight. The complexity of the task arises from its multi-objective nature, the large exploration space, and the sim-to-real gap. To address these challenges, we propose a two-stage reinforcement learning (RL) pipeline. In the first stage, we randomly search for a reward function that balances key objectives: directed flight, obstacle avoidance, formation maintenance, and zero-shot policy deployment. The second stage applies this reward function to more complex scenarios and utilizes curriculum learning to accelerate policy training. Additionally, we incorporate an attention-based observation encoder to improve formation maintenance and adaptability to varying obstacle densities. Experimental results in both simulation and real-world environments demonstrate that our method outperforms both planning-based and RL-based baselines in terms of collision-free rates and formation maintenance across static, dynamic, and mixed obstacle scenarios. Ablation studies further confirm the effectiveness of our curriculum learning strategy and attention-based encoder. Animated demonstrations are available at: https://sites.google.com/view/uav-formation-with-avoidance/.

Large Vision-Language Action (VLA) models have shown significant potential for embodied AI. However, their predominant training via supervised fine-tuning (SFT) limits generalization due to susceptibility to compounding errors under distribution shifts. Reinforcement learning (RL) offers a path to overcome these limitations by optimizing for task objectives via trial-and-error, yet a systematic understanding of its specific generalization benefits for VLAs compared to SFT is lacking. To address this, our study introduces a comprehensive benchmark for evaluating VLA generalization and systematically investigates the impact of RL fine-tuning across diverse visual, semantic, and execution dimensions. Our extensive experiments reveal that RL fine-tuning, particularly with PPO, significantly enhances generalization in semantic understanding and execution robustness over SFT, while maintaining comparable visual robustness. We identify PPO as a more effective RL algorithm for VLAs than LLM-derived methods like DPO and GRPO. We also develop a simple recipe for efficient PPO training on VLAs, and demonstrate its practical utility for improving VLA generalization. The project page is at https://rlvla.github.io

Robot sports, characterized by well-defined objectives, explicit rules, and dynamic interactions, present ideal scenarios for demonstrating embodied intelligence. In this paper, we present VolleyBots, a novel robot sports testbed where multiple drones cooperate and compete in the sport of volleyball under physical dynamics. VolleyBots integrates three features within a unified platform: competitive and cooperative gameplay, turn-based interaction structure, and agile 3D maneuvering.These intertwined features yield a complex problem combining motion control and strategic play, with no available expert demonstrations.We provide a comprehensive suite of tasks ranging from single-drone drills to multi-drone cooperative and competitive tasks, accompanied by baseline evaluations of representative reinforcement learning (RL), multi-agent reinforcement learning (MARL) and game-theoretic algorithms. Simulation results show that on-policy RL methods outperform off-policy methods in single-agent tasks, but both approaches struggle in complex tasks that combine motion control and strategic play.We additionally design a hierarchical policy which achieves 69.5% win rate against the strongest baseline in the 3 vs 3 task, demonstrating its potential for tackling the complex interplay between low-level control and high-level strategy.To highlight VolleyBots’ sim-to-real potential, we further demonstrate the zero-shot deployment of a policy trained entirely in simulation on real-world drones.

We propose ReinFlow, a simple yet effective online reinforcement learning (RL) framework that fine-tunes a family of flow matching policies for continuous robotic control. Derived from rigorous RL theory, ReinFlow injects learnable noise into a flow policy’s deterministic path, converting the flow into a discrete-time Markov Process for exact and straightforward likelihood computation. This conversion facilitates exploration and ensures training stability, enabling ReinFlow to fine-tune diverse flow model variants stably, including Rectified Flow [34] and Shortcut Models [18], particularly at very few or even one denoising step. We benchmark ReinFlow in representative locomotion and manipulation tasks, including long- horizon planning with visual input and sparse reward. The episode reward of Rectified Flow policies obtained an average net growth of 135.36% after fine-tuning in challenging legged locomotion tasks while saving denoising steps and 82.63% of wall time compared to state-of-the-art diffusion RL fine-tuning method DPPO [42]. The success rate of the Shortcut Model policies in state and visual manipulation tasks achieved an average net increase of 40.34% after fine-tuning with ReinFlow at four or even one denoising step, whose performance is comparable to fine-tuned DDIM policies while saving computation time for an average of 23.20% . Code, model, and checkpoints available on the project website: https://reinflow.github.io/

Self-play (SP) is a popular multi-agent reinforcement learning framework for competitive games. Despite the empirical success, the theoretical properties of SP are limited to two-player settings. For team competitive games where two teams of cooperative agents compete with each other, we show a counter-example where SP cannot converge to a global Nash equilibrium (NE) with high probability. Policy-Space Response Oracles (PSRO) is an alternative framework that finds NEs by iteratively learning the best response (BR) to previous policies. PSRO can be directly extended to team competitive games with unchanged convergence properties by learning team BRs, but its repeated training from scratch makes it hard to scale to complex games. In this work, we propose Generalized Fictitious Cross-Play (GFXP), a novel algorithm that inherits benefits from both frameworks. GFXP simultaneously trains an SP-based main policy and a counter population. The main policy is trained by fictitious self-play and cross-play against the counter population, while the counter policies are trained as the BRs to the main policy's checkpoints. We evaluate GFXP in matrix games and gridworld domains where GFXP achieves the lowest exploitabilities. We further conduct experiments in a challenging football game where GFXP defeats SOTA models with over 94% win rate.

Learning Nash equilibrium (NE) in complex zero-sum games with multi-agent reinforcement learning (MARL) can be extremely computationally expensive. Curriculum learning is an effective way to accelerate learning, but an under-explored dimension for generating a curriculum is the difficulty-to-learn of the subgames –games induced by starting from a specific state. In this work, we present a novel subgame curriculum learning framework for zero-sum games. It adopts an adaptive initial state distribution by resetting agents to some previously visited states where they can quickly learn to improve performance. Building upon this framework, we derive a subgame selection metric that approximates the squared distance to NE values and further adopt a particle-based state sampler for subgame generation. Integrating these techniques leads to our new algorithm, Subgame Automatic Curriculum Learning (SACL), which is a realization of the subgame curriculum learning framework. SACL can be combined with any MARL algorithm such as MAPPO. Experiments in the particle-world environment and Google Research Football environment show SACL produces much stronger policies than baselines. In the challenging hide-and-seek quadrant environment, SACL produces all four emergent stages and uses only half the samples of MAPPO with self-play. The project website is at https://sites.google.com/view/sacl-neurips.

Agents built with large language models (LLMs) have shown great potential across a wide range of domains. However, in complex decision-making tasks, pure LLM-based agents tend to exhibit intrinsic bias in their choice of actions, which is inherited from the model’s training data and results in suboptimal performance. To develop strategic language agents, i.e., agents that generate flexible language actions and possess strong decision-making abilities, we propose a novel framework that powers LLM-based agents with reinforcement learning (RL). We consider Werewolf, a popular social deduction game, as a challenging testbed that emphasizes versatile communication and strategic gameplay. To mitigate the intrinsic bias in language actions, our agents use an LLM to perform deductive reasoning and generate a diverse set of action candidates. Then an RL policy trained to optimize the decision-making ability chooses an action from the candidates to play in the game. Extensive experiments show that our agents overcome the intrinsic bias and outperform existing LLM-based agents in the Werewolf game. We also conduct human-agent experiments and find that our agents achieve human-level performance and demonstrate strong strategic play.

Reinforcement Learning from Human Feedback (RLHF) is currently the most widely used method to align large language models (LLMs) with human preferences. Existing RLHF methods can be roughly categorized as either reward-based or reward-free. Novel applications such as ChatGPT and Claude leverage reward-based methods that first learn a reward model and apply actor-critic algorithms, such as Proximal Policy Optimization (PPO). However, in academic benchmarks, state-of-the-art results are often achieved via reward-free methods, such as Direct Preference Optimization (DPO). Is DPO truly superior to PPO? Why does PPO perform poorly on these benchmarks? In this paper, we first conduct both theoretical and empirical studies on the algorithmic properties of DPO and show that DPO may have fundamental limitations. Moreover, we also comprehensively examine PPO and reveal the key factors for the best performances of PPO in fine-tuning LLMs. Finally, we benchmark DPO and PPO across a collection of RLHF testbeds, ranging from dialogue to code generation. Experiment results demonstrate that PPO is able to surpass other alignment methods in all cases and achieve state-of-the-art results in challenging code competitions.

We aim to control a robot to physically behave in the real world following any high-level language command like "cartwheel" or "kick". Although human motion datasets exist, this task remains particularly challenging since generative models can produce physically unrealistic motions, which will be more severe for robots due to different body structures and physical properties. Deploying such a motion to a physical robot can cause even greater difficulties due to the sim2real gap. We develop LAnguage-Guided mOtion cONtrol (LAGOON), a multi-phase reinforcement learning (RL) method to generate physically realistic robot motions under language commands. LAGOON first leverages a pretrained model to generate a human motion from a language command. Then an RL phase trains a control policy in simulation to mimic the generated human motion. Finally, with domain randomization, our learned policy can be deployed to a quadrupedal robot, leading to a quadrupedal robot that can take diverse behaviors in the real world under natural language commands.

Truss layout design, namely finding a lightweight truss layout satisfying all the physical constraints, is a fundamental problem in the building industry. Generating the optimal layout is a challenging combinatorial optimization problem, which can be extremely expensive to solve by exhaustive search. Directly applying end-to-end reinforcement learning (RL) methods to truss layout design is infeasible either, since only a tiny portion of the entire layout space is valid under the physical constraints, leading to particularly sparse rewards for RL training. In this paper, we develop AutoTruss, a two-stage framework to efficiently generate both lightweight and valid truss layouts. AutoTruss first adopts Monte Carlo tree search to discover a diverse collection of valid layouts. Then RL is applied to iteratively refine the valid solutions. We conduct experiments and ablation studies in popular truss layout design test cases in both 2D and 3D settings. AutoTruss outperforms the best-reported layouts by 25.1% in the most challenging 3D test cases, resulting in the first effective deep-RL-based approach in the truss layout design literature.

Recent algorithms designed for multi-agent tasks focus on finding a single optimal solution for all the agents. However, in many tasks (e.g., matrix games and transportation dispatching), there may exist more than one optimal solution, while previous algorithms can only converge to one of them. In many practical applications, it is important to develop reasonable agents with diverse behaviors. In this paper, we propose ”variational multi-agent policy diversification” (VMAPD), an on-policy framework for discovering diverse policies for coordination patterns of multiple agents. By taking advantage of latent variables and exploiting the connection between variational inference and multi-agent reinforcement learning, we derive a tractable evidence lower bound (ELBO) on the trajectories of all agents. Our algorithm uses policy iteration to maximize the derived lower bound and can be simply implemented by adding a pseudo reward during centralized learning. And the trained agents do not need to access the pseudo reward during decentralized execution. We demonstrate the effectiveness of our algorithm on several popular multi-agent testbeds. Experimental results show that VMAPD finds more solutions with similar sample complexity compared with other baselines.

Visual indoor exploration requires agents to explore a room in a limited time. Currently, planning-based solutions have a time-consuming inference stage and require many handcrafted parameters in different scenes. Reinforcement Learning (RL) schemes on the other hand solve these problems by automatically updating flexible policies and affording faster inference time. Spurred by the advantages of RL, we introduce Spatial Attention Visual Exploration (SAVE), which is based on Active Neural SLAM (ANS) [1]. Specifically, we propose a novel RL-based global planner named Spatial Global Policy (SGP) that utilizes spatial information to promote efficient exploration through global goal guidance. SGP has two major components: a transformer-based spatial-attention module encoding spatial interrelation between the agent and different regions to perform spatial reasoning, and a hierarchical spatial action selector to infer global goals for faster training. The map representations are aligned through our spatial adjustor. Experiments on the Habitat photo-realistic simulator [2] demonstrate that SAVE outperforms current planning-based methods and RL variants, reducing at least 10% of the processing steps, 15% of the repeat ratio, and affording an x2 to x4 faster execution time than planning-based methods.

Many advances in cooperative multi-agent reinforcement learning (MARL) are based on two common design principles: value decomposition and parameter sharing. A typical MARL algorithm of this fashion decomposes a centralized Q-function into local Q-networks with parameters shared across agents. Such an algorithmic paradigm enables centralized training and decentralized execution (CTDE) and leads to efficient learning in practice. Despite all the advantages, we revisit these two principles and show that in certain scenarios, e.g., environments with a highly multi-modal reward landscape, value decomposition, and parameter sharing can be problematic and lead to undesired outcomes. In contrast, policy gradient (PG) methods with individual policies provably converge to an optimal solution in these cases, which partially supports some recent empirical observations that PG can be effective in many MARL testbeds. Inspired by our theoretical analysis, we present practical suggestions on implementing multi-agent PG algorithms for either high rewards or diverse emergent behaviors and empirically validate our findings on a variety of domains, ranging from the simplified matrix and grid-world games to complex benchmarks such as StarCraft Multi-Agent Challenge and Google Research Football. We hope our insights could benefit the community towards developing more general and more powerful MARL algorithms.

Proximal Policy Optimization (PPO) is a ubiquitous on-policy reinforcement learning algorithm but is significantly less utilized than off-policy learning algorithms in multi-agent settings. This is often due to the belief that PPO is significantly less sample efficient than off-policy methods in multi-agent systems. In this work, we carefully study the performance of PPO in cooperative multi-agent settings. We show that PPO-based multi-agent algorithms achieve surprisingly strong performance in four popular multi-agent testbeds: the particle-world environments, the StarCraft multi-agent challenge, the Hanabi challenge, and Google Research Football, with minimal hyperparameter tuning and without any domain-specific algorithmic modifications or architectures. Importantly, compared to competitive off-policy methods, PPO often achieves competitive or superior results in both final returns and sample efficiency. Finally, through ablation studies, we analyze implementation and hyperparameter factors that are critical to PPO's empirical performance, and give concrete practical suggestions regarding these factors. Our results show that when using these practices, simple PPO-based methods are a strong baseline in cooperative multi-agent reinforcement learning. Source code is released at https://github.com/marlbenchmark/on-policy.

Multirobot exploration (MR-Exploration) is a primary task providing the location and map for many multirobot applications. To improve system performance, convolutional neural network (CNN) is introduced by recent researches into critical components in MR-Exploration, such as feature-point extraction (FE) and place recognition (PR). This CNN-based MR-Exploration needs to simultaneously run multiple CNN models and complex postprocessing algorithms. This significantly challenges the hardware platforms of embedded systems. Previous researches reveal that an FPGA is ideal for CNN processing on embedded platforms. Such accelerators usually process different models in sequence, while they cannot schedule multiple tasks at runtime. Furthermore, the postprocessing of CNNs is computationally intensive and becomes the bottleneck of the whole system. To handle such problems, we propose an interruptible CNN accelerator for multirobot exploration (INCAME) framework to rapidly deploy the robot applications on FPGAs. In INCAME, we propose an interrupt method based on virtual instructions to support multitasking on CNN accelerators. INCAME also includes hardware modules for accelerating the postprocessing of the CNN-based components. Organically, it integrates the postprocessing and CNN backbone by sharing memory. Experimental results reveal that INCAME enables multitask scheduling on the CNN accelerator with negligible performance degradation (0.3%). INCAME enables embedded FPGAs to perform MR-Exploration in real time (20 fps) via the multitask support and postprocessing acceleration.

In recent years, Convolutional Neural Network (CNN) has been widely used in robotics, which has dramatically improved the perception and decision-making ability of robots. A series of CNN accelerators have been designed to implement energy-efficient CNN on embedded systems. However, despite the high energy efficiency on CNN accelerators, it is difficult for robotics developers to use it. Since the various functions on the robot are usually implemented independently by different developers, simultaneous access to the CNN accelerator by these multiple independent processes will result in hardware resources conflicts.To handle the above problem, we propose an INterruptible CNN Accelerator (INCA) to enable multi-tasking on CNN accelerators. In INCA, we propose a Virtual-Instruction-based interrupt method (VI method) to support multi-task on CNN accelerators. Based on INCA, we deploy the Distributed Simultaneously Localization and Mapping (DSLAM) on an embedded FPGA platform. We use CNN to implement two key components in DSLAM, Feature-point Extraction (FE) and Place Recognition (PR), so that they can both be accelerated on the same CNN accelerator. Experimental results show that, compared to the layer-by-layer interrupt method, our VI method reduces the interrupt respond latency to 1%.

Feature-point extraction is a fundamental step in many applications, such as image matching and Simultaneous Localization and Mapping (SLAM). The CNN-based feature-point extraction methods have made significant signs of progress in both feature-point detection and descriptor generation compared with handcrafted processes. However, the computational and storage complexity makes it difficult for CNN to run on real-time embedded systems. In this paper, we aim to deploy the advanced CNN-based feature-point extraction methods onto real-time embedded FPGA systems. We optimize the softmax data flow so that the computation of softmax and NMS can be reduced by 64×. We generate the normalized descriptors after picking the feature-points with the highest confidence so that the computation cost of normalization is reduced by 1500×. We use fixed-point in both of the CNN backbone and the postprocessing operations, and implement them on the ZCU102 FPGA platform. The experimental results show that our proposed hardware-software co-design CNN-based feature-point extraction method outperforms the handcrafted techniques. Our feature-point extraction on the embedded platform runs at the speed of 20 fps, meeting the real-time requirement.

Multi-Robot Exploration (MR-Exploration) that provides the location and map is a basic task for many multi-robot applications. Recent researches introduce Convolutional Neural Network (CNN) to critical components in MR-Exploration, like Feature-point Extraction (FE) and Place Recognition (PR), to improve the system performance. Such CNN-based MR-Exploration requires running multiple CNN models simultaneously, together with complex post-processing algorithms, greatly challenges the hardware platforms, which are usually embedded systems. Previous researches have shown that FPGA is a good candidate for CNN processing on embedded platforms. But such accelerators usually process different models sequentially, lacking the ability to schedule multiple tasks at runtime. Furthermore, post-processing of CNNs in FE is also computation consuming and becomes the system bottleneck after accelerating the CNN models. To handle such problems, we propose an INterruptible CNN Accelerator for Multi-Robot Exploration (INCAME) framework for rapid deployment of robot applications on FPGA. In INCAME, we propose a virtual-instruction-based interrupt method to support multi-task on CNN accelerators. INCAME also includes hardware modules to accelerate the post-processing of the CNN-based components. Experimental results show that INCAME enables multi-task scheduling on the CNN accelerator with negligible performance degradation (0.3%). With the help of multi-task supporting and post-processing acceleration, INCAME enables embedded FPGA to execute MR-Exploration in real time (20 fps).

Simultaneous Localization and Mapping (SLAM) is considered to be a fundamental capability for intelligent mobile robots. Over the past decades, many impressed SLAM systems have been developed and achieved good performance under certain circumstances. However, some problems are still not well solved, for example, how to tackle the moving objects in the dynamic environments, how to make the robots truly understand the surroundings and accomplish advanced tasks. In this paper, a robust semantic visual SLAM towards dynamic environments named DS-SLAM is proposed. Five threads run in parallel in DS-SLAM: tracking, semantic segmentation, local mapping, loop closing and dense semantic map creation. DS-SLAM combines semantic segmentation network with moving consistency check method to reduce the impact of dynamic objects, and thus the localization accuracy is highly improved in dynamic environments. Meanwhile, a dense semantic octo-tree map is produced, which could be employed for high-level tasks. We conduct experiments both on TUM RGB-D dataset and in real-world environment. The results demonstrate the absolute trajectory accuracy in DS-SLAM can be improved one order of magnitude compared with ORB-SLAM2. It is one of the state-of-the-art SLAM systems in high-dynamic environments.