Understanding unfamiliar codebases is essential yet challenging in software development. Visual aids such as call graphs and control flow graphs can help, but often lead to information overload and limited interactivity. Meanwhile, LLM-based code assistants provide accessible natural language explanations that reduce cognitive barriers, but lack spatial context for code navigation. We posit that integrating these two complementary approaches can address their respective limitations. To validate this integration, we introduce CodeVoyager, a tool that combines LLM with interactive visual aids to support more effective code comprehension. We first conducted an exploratory study (n=11) to assess the tool’s potential and identify areas for refinement. Following iterative refinement, we evaluated the enhanced tool against a widely used chat-based code assistant in a within-subjects study (n=16). Results showed that CodeVoyager improved code comprehension and increased user trust. These improvements were achieved by enabling seamless interaction between textual explanations and visual code exploration, mirroring how developers naturally discuss code. This work contributes to visual-LLM integrated developer tools through (1) a novel integration approach mirroring natural code discussion, (2) empirical evidence of improved comprehension and trust, and (3) design implications for multimodal code comprehension systems.

Aerial manipulation for safe physical interaction with their environments is gaining significant momentum in robotics research. In this paper, we present a disturbance-observer-based safety-critical control for a fully actuated aerial manipulator interacting with both static and dynamic structures. Our approach centers on a safety filter that dynamically adjusts the desired trajectory of the vehicle's pose, accounting for the aerial manipulator's dynamics, the disturbance observer's structure, and motor thrust limits. We provide rigorous proof that the proposed safety filter ensures the forward invariance of the safety set—representing motor thrust limits—even in the presence of disturbance estimation errors. To demonstrate the superiority of our method over existing control strategies for aerial physical interaction, we perform comparative experiments involving complex tasks, such as pushing against a static structure and pulling a plug firmly attached to an electric socket. Furthermore, to highlight its repeatability in scenarios with sudden dynamic changes, we perform repeated tests of pushing a movable cart and extracting a plug from a socket. These experiments confirm that our method not only outperforms existing methods but also excels in handling tasks with rapid dynamic variations.

We present an avatar animation technique for a telepresence system that allows for the hand contact, especially handshaking, between remote users. The key idea is that, while the avatar follows the remote user's motion normally, it modifies the motion to create and maintain hand contact with the local user when the two users try to engage hand contact. To this end, we develop the support vector machine (SVM)-based classifiers to recognize the users' intention for contact interaction, and online motion generation method to create realistic image sequence of an avatar to realize the continuous contact with the user. A user study has been conducted to verify the effect of our method on the social telepresence.

In augmented reality (AR) applications, a virtual avatar serves as a useful medium to represent a human in a different place. This paper deals with the problem of retargeting a human motion to an avatar. In particular, we present a novel method that retargets a human motion with respect to an object to that of an avatar with respect to a different object of a similar shape. To achieve this, we developed a spatial map that defines the correspondences between any points in the 3D spaces around the respective objects. The key advantage of the spatial map is that it identifies the desired locations of the avatar's body parts for any input motion of a human. Once the spatial map is created offline, the motion retargeting can be performed in real-time. The retargeted motion preserves important features of the original motion such as the human pose and the spatial relation with the object. We report the results of a number of experiments that demonstrate the effectiveness of the proposed method.

Social touch such as a handshake increases the sense of coexistence and closeness between remote users in a social telepresence environment, but creating such coordinated contact movements with a distant person is extremely difficult if given only visual feedback, without haptic feedback. This paper presents a method to enable hand-contact interaction between remote users in an avatar-mediated telepresence environment. The key approach is, while the avatar directly follows its owner's motion in normal conditions, it adjusts the pose to maintain contact with the other user when the two users attempt to make contact interaction. To this end, we develop classifiers to recognize the users' intention for the contact interaction. The contact classifier identifies whether the users try to initiate contact when they are not in contact, and the separation classifier identifies whether the two in contact attempt to break contact. The classifiers are trained based on a set of geometric distance features. During the contact phase, inverse kinematics is solved to determine the pose of the avatar's arm so as to initiate and maintain natural contact with the other user's hand. Our system is unique in that two remote users can perform real time hand contact interaction in a social telepresence environment.