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The increasing deployment of robots alongside humans necessitates sophisticated communication and motion planning to ensure safety and task achievability in social navigation scenarios. Existing methods often rely heavily on historical data and extensive expert hand-coding, which limits their scalability and generalizability. This paper introduces a novel framework that models social navigation as a Markov Decision Process (MDP), utilizing Conditional Abstraction Trees (CATs) to learn dynamic abstract world representations and policies that focus on critical aspects of interaction. In the offline phase, the framework operates within a simulator, while in the online phase, it deploys the learned representations and policies in real-world scenarios for ongoing refinement and adaptation. Integral to our approach is a Dynamic Bayesian Network (DBN) based human sensor and belief model that accounts for humans’ imperfect perception to enhance the prediction of human motion. We evaluated our method through extensive simulations and user studies involving physical experiments, demonstrating its effectiveness in managing critical interactions and ensuring safety and task completion across various scenarios. 

This article introduces a model-based approach for training feedback controllers for an autonomous agent operating in a highly non-linear (albeit deterministic) environment. We desire the trained policy to ensure that the agent satisfies specific task objectives and safety constraints, both expressed in Discrete-Time Signal Temporal Logic (DT-STL). One advantage for reformulation of a task via formal frameworks, like DT-STL, is that it permits quantitative satisfaction semantics. In other words, given a trajectory and a DT-STL formula, we can compute therobustness, which can be interpreted as an approximate signed distance between the trajectory and the set of trajectories satisfying the formula. We utilize feedback control, and we assume a feed forward neural network for learning the feedback controller. We show how this learning problem is similar to training recurrent neural networks (RNNs), where the number of recurrent units is proportional to the temporal horizon of the agent’s task objectives. This poses a challenge: RNNs are susceptible to vanishing and exploding gradients, and naïve gradient descent-based strategies to solve long-horizon task objectives thus suffer from the same problems. To address this challenge, we introduce a novel gradient approximation algorithm based on the idea of dropout or gradient sampling. One of the main contributions is the notion ofcontroller network dropout, where we approximate the NN controller in several timesteps in the task horizon by the control input obtained using the controller in a previous training step. We show that our control synthesis methodology can be quite helpful for stochastic gradient descent to converge with less numerical issues, enabling scalable back-propagation over longer time horizons and trajectories over higher-dimensional state spaces. We demonstrate the efficacy of our approach on various motion planning applications requiring complex spatio-temporal and sequential tasks ranging over thousands of timesteps. 

In this paper, we present a decentralized control approach based on a Nonlinear Model Predictive Control (NMPC) method that employs barrier certificates for safe navigation of multiple nonholonomic wheeled mobile robots in unknown environments with static and/or dynamic obstacles. This method incorporates a Learned Barrier Function (LBF) into the NMPC design in order to guarantee safe robot navigation, i.e., prevent robot collisions with other robots and the obstacles. We refer to our proposed control approach as NMPC-LBF. Since each robot does not have a priori knowledge about the obstacles and other robots, we use a Deep Neural Network (DeepNN) running in real-time on each robot to learn the Barrier Function (BF) only from the robot's LiDAR and odometry measurements. The DeepNN is trained to learn the BF that separates safe and unsafe regions. We implemented our proposed method on simulated and actual Turtlebot3 Burger robot(s) in different scenarios. The implementation results show the effectiveness of the NMPC-LBF method at ensuring safe navigation of the robots. 

Temporal Logic (TL) bridges the gap between natural language and formal reasoning in the field of complex systems verification. However, in order to leverage the expressivity entailed by TL, the syntax and semantics must first be understood—a large task in itself. This significant knowledge gap leads to several issues: (1) the likelihood of adopting a TL-based verification method is decreased, and (2) the chance of poorly written and inaccurate requirements is increased. In this ongoing work, we present the Pythonic Formal Requirements Language (PyFoReL) tool: a Domain-Specific Language inspired by the programming language Python to simplify the elicitation of TL-based requirements for engineers and non-experts.