30 accepted papers
We present LoTIS, a model for visual navigation that provides robot-agnostic image-space guidance by localizing a reference RGB trajectory in the robot’s current view, without requiring camera calibration, poses, or robot-specific training. Instead of predicting actions tied to specific robots, we p…
Robots deployed in dynamic environments must remain safe even when key physical parameters are uncertain or change over time. We propose Parameter-Robust Model Predictive Path Integral (PRMPPI) control, a framework that integrates online parameter learning with probabilistic safety constraints. PRMP
In the near future, most deployed spacecraft will be autonomous. Their tasks will involve autonomous rendezvous and proximity operations (RPOs) with large structures, such as inspection, assembly, and maintenance of orbiting space stations, as well as human-assistance tasks over shared workspaces. Y…
We present an experimental validation framework for space robotics that leverages underwater environments to approximate microgravity dynamics. While neutral buoyancy conditions make underwater robotics an excellent platform for space robotics validation, there are still dynamical and environmental …
Uncertainties in contact dynamics and object geometry remain significant barriers to robust robotic manipulation. Caging helps mitigate these uncertainties by constraining an object’s mobility without requiring precise contact modeling. Existing caging research often treats morphology and policy opt
We present a planning and control approach for collaborative transportation of objects in space by a team of robots. Object and robots in microgravity environments are not subject to friction but are instead free-floating. This property is key to how we approach the transportation problem: the passi…
Informative path planning (IPP) applied to bathy-metric mapping allows AUVs to focus on feature-rich areas to quickly reduce uncertainty and increase mapping efficiency. Existing methods based on Bayesian optimization (BO) over Gaussian Process (GP) maps work well on small scenarios but they are sho
Useful robot control algorithms should not only achieve performance objectives but also adhere to hard safety constraints. Control Barrier Functions (CBFs) have been developed to provably ensure system safety through forward invariance. However, they often unnecessarily sacrifice performance for saf
Limited visibility and sensor occlusions pose pressing safety challenges for advanced driver-assistance systems (ADAS) and autonomous vehicles (AVs). In this work, our pursuit was to strike a balance: a method that ensures safety in occluded scenarios while preventing overly cautious behavior. We ar…
We present the integration of a Non-Axiomatic Reasoning System (NARS) with mobile robots for planning and decision making. NARS enables robots to effectively handle uncertainty in real-time with complete sensor and actuator integration, thereby ensuring adaptability to evolving scenarios. We discuss…
This paper addresses the problem of safety-critical control of autonomous robots, considering the ubiquitous uncertainties arising from un-modeled dynamics and noisy sensors. To take into account these uncertainties, probabilistic state estimators are often deployed to obtain a belief over possible…
Partial observability and uncertainty are common problems in sequential decision-making that particularly impede the use of formal models such as Markov decision processes (MDPs). However, in practice, agents may be able to employ costly sensors to measure their environment and resolve partial obser…
In environments like offices, the duration of a robot’s navigation between two locations may vary over time. For instance, reaching a kitchen may take more time during lunchtime since the corridors are crowded with people heading the same way. In this work, we address the problem of routing in such…
Practical implementations of deep reinforcement learning (deep RL) have been challenging due to an amplitude of factors, such as designing reward functions that cover every possible interaction. To address the heavy burden of robot reward engineering, we aim to leverage subjective human preferences…
Ensuring safety in real-world robotic systems is often challenging due to unmodeled disturbances and noisy sensors. To account for such stochastic uncertainties, many robotic systems leverage probabilistic state estimators such as Kalman filters to obtain a robot's <italic xmlns:mml="http://www.w3.o
We focus on correct-by-design robot task planning from finite Linear Temporal Logic (LTLf) specifications with a human in the loop. Since provable guarantees are difficult to obtain unconditionally, we take an assume-guarantee perspective. Along with guarantees on the robot's task satisfaction, we c…
Rearranging objects is an essential skill for robots. To quickly teach robots new rearrangements tasks, we would like to generate training scenarios from high-level specifications that define the relative placement of objects for the task at hand. Ideally, to guide the robot's learning we also want…
Signal Temporal Logic (STL) is a rigorous specification language that allows one to express various spatio-temporal requirements and preferences. Its semantics (called robustness) allows quantifying to what extent are the STL specifications met. In this work, we focus on enabling STL constraints and…
In many real-world robotic scenarios, we cannot assume exact knowledge about a robot's state due to unmodeled dynamics or noisy sensors. Planning in belief space addresses this problem by tightly coupling perception and planning modules to obtain trajectories that take into account the environment's…
Human-in-the-loop reinforcement learning (RL) methods actively integrate human knowledge to create reward functions for various robotic tasks. Learning from preferences shows promise as alleviates the requirement of demonstrations by querying humans on state-action sequences. However, the limited gr…
Despite the successes of deep reinforcement learning (RL), it is still challenging to obtain safe policies. Formal verification approaches ensure safety at all times, but usually overly restrict the agent’s behaviors, since they assume adversarial behavior of the environment. Instead of assuming adv
This paper is interested in formalizing human trajectories in human-robot encounters. Inspired by robot navigation tasks in human-crowded environments, we consider the case where a human and a robot walk towards each other, and where humans have to avoid colliding with the incoming robot. Further, h…
Driving styles play a major role in the acceptance and use of autonomous vehicles. Yet, existing motion planning techniques can often only incorporate simple driving styles that are modeled by the developers of the planner and not tailored to the passenger. We present a new approach to encode human…
In this paper, we are interested in formalizing human trajectories in human-robot encounters. We consider a particular case where a human and a robot walk towards each other. A question that arises is whether, when, and how humans will deviate from their trajectory to avoid a collision. These human…
Unknown properties of a robot’s environment are one of the sources of uncertainty in autonomous navigation. This uncertainty has to be accounted for when modelling robot dynamics. For ground vehicles in particular, terrain structure is one of the main environmental factors that can strongly influenc…
Complex mission specifications can be often specified through temporal logics; such as Linear Temporal Logic and its syntactically co-safe fragment; scLTL. Finding trajectories that satisfy such specifications becomes hard if the robot is to fulfil the mission in an initially unknown environment; wh…
Algorithms for autonomous robotic exploration usually focus on optimizing time and coverage, often in a greedy fashion. However, obstacle inflation is conservative and might limit mapping capabilities and even prevent the robot from moving through narrow, important places. This letter proposes a met
In this paper we consider a fleet of self-driving cars operating in a road network governed by rules of the road, such as the Vienna Convention on Road Traffic, providing rides to customers to serve their demands with desired deadlines. We focus on the associated motion planning problem that trades-…
This work focuses on integrated routing and motion planning for an autonomous vehicle in a road network. We consider a problem in which customer demands need to be met within desired deadlines, and the rules of the road need to be satisfied. The vehicle might not, however, be able to satisfy these t…
In this paper, we study the decentralized control problem of a two-agent system under local goal specifications given as temporal logic formulas. The agents collaboratively carry an object in a leader-follower scheme and lack means to exchange messages on-line, i.e., to communicate explicitly. Speci…