MIT’s AI Robotics Lab Director Is Building People-Centered Robots

Daniela Rus has spent her career breaking barriers—scientific, social, and material—in her quest to build machines that amplify rather than replace human capability. She made robotics her life’s work, she says, because she understood it was a way to expand the possibilities of computing while enhancing human capabilities.“I like to think of robotics as a…

Safety Meets Speed: Accelerated Neural MPC With Safety Guarantees and No Retraining

While Model Predictive Control (MPC) enforces safety via constraints, its real-time execution can exceed embedded compute budgets. We propose a Barrier-integrated Adaptive Neural Model Predictive Control (BAN-MPC) framework that synergizes neural networks’ fast computation with MPC’s constraint-handling capability. To ensure strict safety, we replace traditional Euclidean distance with Control Barrier Functions (CBFs) for collision avoidance.…

Adaptive Gradient Neural Networks for Solving the Time-Varying Sylvester Equation

This paper develops several new dynamical designs, based on the gradient neural network (GNN), from the perspective of control theory to solve the time-varying Sylvester equation (TVSE). We start with an adaptive gradient neural network called AGNN-S. Then, based on Lyapunov theory, we propose three improved models: ACGNN, AIGNN, and ABGNN. Among them, the ABGNN…

Plan Optimal Collision-Free Trajectories With Nonconvex Cost Functions Using Graphs of Convex Sets

The recently developed approach to motion planning in graphs of convex sets (GCS) provides an efficient framework for computing shortest-distance collision-free paths using convex optimization. This new motion planner is notably more computationally efficient than popular sampling-based motion planners, but it does not support nonconvex cost functions. This article develops a novel motion planning algorithm,…

Computationally Efficient Bayesian Model Predictive Control for 4-D Flight Trajectory Tracking Under Windy Conditions

Accurate 4-D trajectory tracking can improve trajectory predictability and further enhance the efficiency of air traffic management (ATM). However, the tracking accuracy is inevitably affected by uncertainties arising from wind. To maintain tracking performance, a novel Bayesian model predictive control (MPC) framework is proposed, within which a Bayesian recurrent neural network-based probabilistic wind prediction module…

Deep Visual Odometry for Stereo Event Cameras

Event-based cameras are bio-inspired sensors with pixels that independently and asynchronously respond to brightness changes at microsecond resolution, offering the potential to handle state estimation tasks involving motion blur and high dynamic range (HDR) illumination conditions. However, the versatility of event-based visual odometry (VO) relying on handcrafted data association (either direct or indirect methods) is…

Reinforcement Learning-Based Optimal Formation Control for Multiple WMRs With Visual Servoing

In this paper, a reinforcement learning (RL) control method is developed for the formation control of multiple wheeled mobile robots (WMRs) with visual servoing. First, a multi-robot system model is constructed based on the kinematic models of mobile robots, the camera model, and the multiple-view geometry principles. The leader-follower structure is then applied to derive…

Multi-Agents Cooperative Target Tracking Under Physical Attacks With Environment-Aware Dynamic Constraints

In this work, we consider a cooperative target tracking problem by multi-agents in a 3D space, under disruptions from multiple physical attackers, where the target and physical attackers are intelligent, that is, they adjust velocities based on relative coordinates with agents and agents’ velocities. Due to the presence of physical attackers, some safety and performance…

SWIFT: A Distributed One-Stage Planner for Efficient Multi-Quadrotor Trajectory Optimization

This paper presents SWIFT (Swarm-Wise Inference for Fast Trajectory Planning), a distributed one-stage planner designed for efficient multi-quadrotor trajectory optimization in cluttered environments. SWIFT unifies depth-based perception, interaction-aware modeling, and trajectory prediction into a single lightweight network, enabling decentralized real-time planning without reliance on global maps. Each quadrotor processes its local depth observation and asynchronously…

Large Behavior Models Are Helping Atlas Get to Work

Boston Dynamics can be forgiven, I think, for the relative lack of acrobatic prowess displayed by the new version of Atlas in (most of) its latest videos. In fact, if you look at this Atlas video from late last year and compare it to Atlas’s most recent video, it’s doing what looks to be more…