Robot navigation systems are critical for various real-world applications such as delivery services, hospital logistics, and warehouse management. Although classical navigation methods provide interpretability, they rely heavily on expert manual tuning, limiting their adaptability. Conversely, purely learning-based methods offer adaptability but often lead to instability and erratic robot behaviors. Recently introduced parameter tuners aim to balance these approaches by integrating data-driven adaptability into classical navigation frameworks. However, the parameter tuning process currently suffers from training inefficiencies and redundant sampling, with critical regions in environment often underrepresented in training data.In this paper, we propose EffiTune, a novel framework designed to diagnose and mitigate training inefficiency for parameter tuners in robot navigation systems. EffiTune first performs robot-behavior-guided diagnostics to pinpoint critical bottlenecks and underrepresented regions. It then employs a targeted up-sampling strategy to enrich the training dataset with critical samples, significantly reducing redundancy and enhancing training efficiency. Our comprehensive evaluation demonstrates that EffiTune achieves more than a 13.5% improvement in navigation performance, enhanced robustness in out-of-distribution scenarios, and a 4× improvement in training efficiency within the same computational budget.

A robotic system continuously measures its own motions and the external world during operation. Such measurements are with respect to some frame of reference, i.e., a coordinate system. A nontrivial robotic system has a large number of different frames and data have to be translated back-and-forth from a frame to another. The onus is on the developers to get such translation right. However, this is very challenging and error-prone, evidenced by the large number of questions and issues related to frame uses on developers' forum. Since any state variable can be associated with some frame, reference frames can be naturally modeled as variable types. We hence develop a novel type system that can automatically infer variables' frame types and in turn detect any type inconsistencies and violations of frame conventions. The evaluation on a set of 180 publicly available ROS projects shows that our system can detect 190 inconsistencies with 154 true positives. We reported 52 to developers and received 18 responses so far, with 15 fixed/acknowledged. Our technique also finds 45 violations of common practices.

We propose a new type of vulnerability for Robotic Vehicles (RVs), called Cyber-Physical Inconsistency. These vulnerabilities target safety checks in RVs (e.g., crash detection). They can be exploited by setting up malicious environment conditions such as placing an obstacle with a certain weight and a certain angle in the RV's trajectory. Once exploited, the safety checks may fail to report real physical accidents or report false alarms (while the RV is still operating normally). Both situations could lead to life-threatening consequences. The root cause of such vulnerabilities is that existing safety checks are mostly using simple range checks implemented in general-purpose programming languages, which are incapable of describing the complex and delicate physical world. We develop a novel technique that requires the interplay of program analysis, vehicle modeling, and search-based testing to identify such vulnerabilities. Our experiment on 4 real-world control software and 8 vehicles including quadrotors, rover, and fixed-wing airplane has discovered 10 real vulnerabilities. Our technique does not have false positives as it only reports when an exploit can be generated.

Program variables used in robotic and cyber-physical systems often have implicit physical units that cannot be determined from their variable types. Inferring an abstract physical unit type for variables and checking their physical unit type consistency is of particular importance for validating the correctness of such systems. For instance, a variable with the unit of ‘meter’ should not be assigned to another variable with the unit of ‘degree-per-second’. Existing solutions have various limitations such as requiring developers to annotate variables with physical units and only handling variables that are directly or transitively used in popular robotic libraries with known physical unit information. We observe that there are a lot of physical unit hints in these softwares such as variable names and specific forms of expressions. These hints have uncertainty as developers may not respect conventions. We propose to model them with probability distributions and conduct probabilistic inference. At the end, our technique produces a unit distribution for each variable. Unit inconsistencies can then be detected using the highly probable unit assignments. Experimental results on 30 programs show that our technique can infer units for 159.3% more variables compared to the state-of-the-art with more than 88.7% true positives, and inconsistencies detection on 90 programs shows that our technique reports 103.3% more inconsistencies with 85.3% true positives.