Qace Dynamics

QACE Dynamics | Deterministic Task Replay We are adding a new execution layer that allows robotic tasks to be replayed and inspected step by step. Each run records planner decisions, map state, sensor input, and control output. This makes it possible to review robot behavior, compare simulation with hardware, and validate task logic without rerunning the robot. The replay system supports pausing, rewinding, and branching from any step, helping developers debug and test autonomy more efficiently. More updates coming soon.

QACE Situation and Team Update We want to address what happened recently and clear up any confusion: After the recent market dump, many KOLs began selling their positions. This created a panic sell cascade, which amplified the price drop. The team is still fully present and actively working to stabilize the situation in the best way we can. We appreciate the community’s patience. We will keep working as usual.

QACE Dynamics | Weekly Progress Update This week marked the transition of navigation from internal testing into hands on beta usage, with a focus on reliability, reuse, and real world behaviour. Autonomous navigation is now available inside the beta dApp. Robots can move using prebuilt maps and onboard sensors, with support for defined locations such as task points or return positions. Once a destination is set, the system handles planning and execution without manual intervention. Navigation now operates through a layered planning approach. A global planner determines the overall route using the map, while a local planner continuously adapts movement based on live lidar and camera input. This ensures safe progress even when unexpected obstacles appear. Navigation targets are now reusable across workflows. Named locations and zones can be referenced by higher level logic, allowing tasks to be built around spatial intent rather than raw coordinates. The same navigation block runs unchanged in simulation and on physical robots, making it possible to validate behaviour in Gazebo and deploy directly to hardware using the same setup. More blocks and workflows coming next.

QACE Dynamics | Navigation and Mapping Enhancement Navigation in QACE has been extended beyond simple goal based movement. Robots now operate with a layered planning pipeline that separates long range route planning from short range obstacle handling. A global planner computes the full path using the map, while a local planner continuously adjusts motion using live lidar and camera data. This allows the robot to keep progressing toward the goal even in dynamic environments. Navigation goals are now treated as first class objects. Named locations, task points, and zones on the map can be reused across different workflows, allowing higher level tasks to reference space directly instead of coordinates. We also unified navigation state across simulation and hardware. The same navigation module runs unchanged in Gazebo and on physical robots, using identical map data, planners, and constraints. This makes testing in simulation directly transferable to real deployments. These changes make navigation more reliable under real world conditions and prepare the stack for multi step tasks that combine movement, perception, and future manipulation blocks.

QACE Dynamics | Autonomous Navigation Live on Beta Autonomous navigation is now live inside QACE. Robots navigate using an existing map and onboard sensors, with support for pinned locations such as rooms, objects, or task points. A target can be selected, and the robot plans and follows the best path from its current position. If new obstacles appear, the robot adjusts locally and continues safely without breaking the task. The same navigation block works in simulation and on real robots. It can be downloaded as a ready to use module and plugged into ROS setups or Gazebo simulations, then deployed on physical hardware using the same workflow.

QACE Dynamics |Autonomous Navigation Live on Beta Wednesday A new autonomous navigation block is going live on the beta dApp this Wednesday. This block allows robots to navigate using their existing maps and onboard sensors. A target location can be set, and the robot plans and follows the optimal path from its current position. If new obstacles appear that are not part of the original map, the robot locally adjusts its path and navigates around them safely. Set points such as home locations can be defined, allowing robots to be called back to predefined positions without collisions. More blocks and workflows coming next.

QACE Dynamics | Robotics Testing & Validation Environment We are excited to share a first look at our robotics testing and validation environment. Captured from our development environment, the footage shows AGVs, robotic arms, grippers, cameras, and sensors being configured and tested as they come online within the QACE stack. These systems will be fully connected to the QACE dApp, making them remotely configurable and accessible. Developers will be able to deploy our default software stacks or upload and run their own, all on shared physical hardware. This is an early preview of what will soon be live inside the QACE Lab. More walkthroughs coming shortly.

QACE Dynamics | Early Look at the Robotics Configuration and Testing Stack Tomorrow, we’ll be sharing a video giving a first look at our robotics testing and validation environment. The setup includes multiple AGVs, grippers, cameras, and lidars. The space shown is still our development environment, but it reflects the foundation we’re building. The lab is designed to be opened to robotic developers, allowing them to rent and use both our hardware and software, as well as connect their own systems directly through the QACE dApp. More details and the full video will be shared tomorrow.

QACE Dynamics | Weekly Progress Update We have been working non-stop, and this has been a particularly important week for QACE. The focus has been on building the core foundation that turns robots into shared, configurable resources within the QACE ecosystem. We have successfully put the reservation and configuration infrastructure in place. Robots can now be allocated per session and prepared with either a user-provided software stack or the default QACE stack. This enables seamless switching between multiple use cases on the same hardware, without any manual reconfiguration. What’s coming next? With token-gated access already live, we are moving into the next phase of beta upgrades. • Additional AI stacks are being integrated, expanding what can be executed through a single trigger and making workflows more powerful and flexible. • We are finalizing real-world walkthroughs videos that show the complete lifecycle: robot reservation, configuration, and live execution on physical hardware. These demos will clearly illustrate how QACE operates end-to-end in real environments. More updates coming very soon.

QACE Dynamics | Token Gated Access Is Now Live Token gated access is now live in the QACE dApp. Users holding at least 0.05% of the supply, which is 500000 QACE tokens, now receive free lifetime access to the platform. This unlocks full access to the QACE ecosystem as it continues to expand. For users who do not hold tokens, a fiat based option will be available. Anyone will be able to rent robots, configure services, and run tests on demand through the dApp. Details around pricing and service options will be shared soon. This access model keeps the platform open while rewarding long term supporters.