I recommend this technical report by Park and Boisgontier. The authors present an innovative adaptation of the approach-avoidance task using the Kinarm robotic platform. Their Kinarm Approach-Avoidance Task (KAAT) enables precise measurement of multiple kinematic variables across multiple reach directions, providing richer behavioral data than traditional keyboard or joystick-based approaches. The robust technical documentation, including detailed task specifications and pilot data, demonstrates Kinarm’s capability to capture nuanced aspects of motor behavior like reaction times, movement speeds, and acceleration patterns. Particularly noteworthy are the potential applications across various clinical domains. This work also complements previous work done by Perry and colleagues, where they used an object hit and avoid task to create a comprehensive statistical model of motor learning. The authors have also thoughtfully included the capability to apply resistive loads during reaching movements. The transparent sharing of materials and code through their GitHub repository also reflects commendable research practices. While the pilot sample is small, the thorough technical foundation laid by this work opens valuable new avenues for investigating approach-avoidance tendencies with a lot of kinematic detail.