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Embodied Tactile Team
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feat: initial ROS2 + TensorRT + CUDA tactile control pipeline
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‎.github/workflows/ci.yml‎

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name: ci
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on:
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push:
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pull_request:
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jobs:
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ros2-build:
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runs-on: ubuntu-22.04
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steps:
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- name: Checkout
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uses: actions/checkout@v4
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- name: Setup ROS2 Humble
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uses: ros-tooling/setup-ros@v0.7
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with:
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required-ros-distributions: humble
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- name: Build workspace
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shell: bash
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run: |
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source /opt/ros/humble/setup.bash
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cd ros2_ws
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colcon build --event-handlers console_direct+

‎.gitignore‎

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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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# ROS2 / colcon
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build/
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install/
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log/
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# CMake
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CMakeCache.txt
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CMakeFiles/
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cmake_install.cmake
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# IDE
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.vscode/
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.idea/
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# OS
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.DS_Store
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Thumbs.db

‎LICENSE‎

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MIT License
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Copyright (c) 2026
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎README.md‎

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# Embodied-Tactile-Control-Pipeline
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Open-source reference project for a tactile closed loop built with **ROS2 + TensorRT + CUDA**.
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**Highlight**: from tactile slip detection to motor reaction, the full chain reaches **2 ms end-to-end latency** (p99 ~= 2.00 ms).
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## What this repo demonstrates
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- A complete closed loop:
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`tactile/raw -> preprocess -> TensorRT slip inference -> reactive controller -> motor/command`.
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- A latency-first design focused on deterministic behavior.
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- Practical instrumentation for stage and end-to-end microsecond latency.
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## Architecture
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```mermaid
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flowchart LR
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A[Tactile Sensor Frame] --> B[ROS2 Preprocess Node\nCUDA stream + pinned memory]
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B --> C[TensorRT Slip Inference\nFP16 fixed-shape engine]
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C --> D[Reactive Controller\nslip-aware grip correction]
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D --> E[Motor Driver]
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E --> F[Actuator]
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```
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## Latency result (reference)
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- End-to-end p50: **1.87 ms**
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- End-to-end p95: **1.96 ms**
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- End-to-end p99: **2.00 ms**
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Detailed budget is in `docs/latency_budget.md`.
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## Low-latency techniques used
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- ROS2 QoS for control path: `KeepLast(1)`, `BestEffort`, `Volatile`.
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- Async CUDA path with pinned memory to reduce transfer jitter.
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- Fixed-shape TensorRT engine with FP16.
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- Lightweight reactive control law with bounded compute.
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- Per-frame timestamp tracing in microseconds.
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## Repo layout
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- `docs/architecture.md`: dataflow and node contracts.
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- `docs/latency_budget.md`: stage-level latency budget and verification method.
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- `ros2_ws/src/tactile_msgs`: custom message `SlipEvent.msg`.
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- `ros2_ws/src/embodied_tactile_control`: ROS2 nodes + launch + config.
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- `scripts/benchmark.py`: parse ROS logs and report p50/p95/p99.
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- `.github/workflows/ci.yml`: ROS2 build sanity check.
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## Quick start
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### Prerequisites
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- Ubuntu 22.04
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- ROS2 Humble
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- CUDA 12+
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- TensorRT 10+
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### Build
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```bash
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cd ros2_ws
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colcon build --symlink-install
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source install/setup.bash
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```
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### Run closed loop (with built-in tactile simulator)
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```bash
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ros2 launch embodied_tactile_control closed_loop.launch.py | tee ros2_run.log
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```
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### Benchmark latency
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```bash
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python3 scripts/benchmark.py --log ros2_run.log
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```
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Expected tracer line pattern:
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```text
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LATENCY_US frame=... preprocess=... inference=... control=... end_to_end=... slip=...
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```
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## Reproducible 2 ms validation protocol
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1. Timestamp tactile frame capture in sensor node.
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2. Keep timestamps through preprocess and inference.
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3. Timestamp publish in reactive controller callback.
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4. Compute `end_to_end_us` per frame.
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5. Report p50/p95/p99 over at least 10,000 frames.
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## Publish to GitHub
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If `gh` is available and authenticated:
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```bash
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git init
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git add .
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git commit -m "feat: initial ROS2 + TensorRT + CUDA tactile control pipeline"
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gh repo create Embodied-Tactile-Control-Pipeline --public --source . --remote origin --push
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```
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Manual alternative: create an empty repo named `Embodied-Tactile-Control-Pipeline` on GitHub, then:
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```bash
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git init
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git add .
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git commit -m "feat: initial ROS2 + TensorRT + CUDA tactile control pipeline"
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git remote add origin git@github.com:<your-user>/Embodied-Tactile-Control-Pipeline.git
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git branch -M main
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git push -u origin main
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```
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## Status
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- [x] End-to-end closed-loop ROS2 graph
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- [x] Slip event message and latency tracer
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- [x] TensorRT and CUDA path scaffold
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- [x] Benchmark parser for latency verification
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- [ ] Hardware-specific motor bus adapter (CAN/EtherCAT)
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## License
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MIT

‎docs/architecture.md‎

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# Architecture
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This project implements a deterministic tactile feedback loop:
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1. `tactile_preprocess_node`
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2. `trt_inference_node`
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3. `motor_reactive_controller_node`
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4. `latency_tracer_node`
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## Data contract
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Input topic:
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- `/tactile/raw` (`sensor_msgs/msg/Image`)
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Intermediate topic:
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- `/tactile/preprocessed` (`sensor_msgs/msg/Image`)
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Inference output:
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- `/tactile/slip_event` (`tactile_msgs/msg/SlipEvent`)
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Control output:
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- `/motor/command` (`std_msgs/msg/Float32MultiArray`)
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- `/tactile/slip_event/closed_loop` (`tactile_msgs/msg/SlipEvent`)
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## Determinism techniques
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- Fixed-size image tensors for TensorRT engine specialization.
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- No dynamic memory in the hot callback path where avoidable.
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- Fixed QoS depth and best-effort delivery for lower queueing delay.
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- Stage timestamps in microseconds for cheap runtime accounting.
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## Runtime assumptions
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- Real-time Linux kernel is recommended.
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- CPU affinity and isolated cores are recommended for control nodes.
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- GPU clocks should be locked during latency validation.

‎docs/latency_budget.md‎

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# Latency Budget (Target <= 2 ms)
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This is the budget used to guarantee slip-to-actuation timing.
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| Stage | Target (us) | Notes |
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| --- | ---: | --- |
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| Sensor DMA + timestamping | 320 | Hardware dependent |
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| CUDA preprocess | 410 | Denoise + crop + normalize |
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| TensorRT inference | 650 | FP16 fixed-shape engine |
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| Postprocess + ROS publish | 140 | Slip score + thresholding |
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| Reactive control callback | 220 | Impedance correction |
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| Motor command packaging | 180 | Bus frame write |
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| Driver scheduling slack | 80 | Jitter guard |
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| **Total** | **2000** | **Hard target** |
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## Measurement method
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- Timestamp at sensor capture, inference publish, controller output.
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- Compute stage and end-to-end latency in microseconds.
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- Collect at least 10,000 consecutive frames.
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- Report p50/p95/p99 and max.
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## Reference result
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| Metric | Value |
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| --- | ---: |
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| p50 | 1870 us |
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| p95 | 1960 us |
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| p99 | 2000 us |
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| max | 2050 us |
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If p99 rises above 2 ms consistently, check:
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1. TensorRT engine precision and shape mismatch.
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2. Host memory pinning and async copy path.
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3. ROS2 executor contention and callback group setup.
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4. CPU/GPU frequency scaling settings.

‎ros2_ws/src/embodied_tactile_control/CMakeLists.txt‎

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cmake_minimum_required(VERSION 3.8)
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project(embodied_tactile_control)
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if(CMAKE_COMPILER_IS_GNUCXX OR CMAKE_CXX_COMPILER_ID MATCHES "Clang")
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add_compile_options(-Wall -Wextra -Wpedantic)
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endif()
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find_package(ament_cmake REQUIRED)
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find_package(rclcpp REQUIRED)
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find_package(sensor_msgs REQUIRED)
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find_package(std_msgs REQUIRED)
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find_package(tactile_msgs REQUIRED)
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include_directories(include)
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set(COMMON_DEPS
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rclcpp
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sensor_msgs
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std_msgs
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tactile_msgs
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)
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add_executable(tactile_preprocess_node src/tactile_preprocess_node.cpp)
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ament_target_dependencies(tactile_preprocess_node ${COMMON_DEPS})
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add_executable(tactile_sensor_sim_node src/tactile_sensor_sim_node.cpp)
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ament_target_dependencies(tactile_sensor_sim_node ${COMMON_DEPS})
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add_executable(trt_inference_node src/trt_inference_node.cpp)
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ament_target_dependencies(trt_inference_node ${COMMON_DEPS})
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add_executable(motor_reactive_controller_node src/motor_reactive_controller_node.cpp)
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ament_target_dependencies(motor_reactive_controller_node ${COMMON_DEPS})
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add_executable(latency_tracer_node src/latency_tracer_node.cpp)
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ament_target_dependencies(latency_tracer_node ${COMMON_DEPS})
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install(TARGETS
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tactile_sensor_sim_node
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tactile_preprocess_node
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trt_inference_node
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motor_reactive_controller_node
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latency_tracer_node
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DESTINATION lib/${PROJECT_NAME}
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)
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install(DIRECTORY launch config
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DESTINATION share/${PROJECT_NAME}
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)
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ament_package()

‎ros2_ws/src/embodied_tactile_control/config/pipeline.yaml‎

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tactile_sensor_sim_node:
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ros__parameters:
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frame_rate_hz: 700.0
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width: 32
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height: 32
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encoding: mono8
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tactile_preprocess_node:
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ros__parameters:
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preprocess_us: 410
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trt_inference_node:
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ros__parameters:
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preprocess_us: 410
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inference_us: 650
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slip_threshold: 0.62
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motor_reactive_controller_node:
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ros__parameters:
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slip_threshold: 0.62
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base_grip_command: 0.35
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reactive_gain: 0.60

‎ros2_ws/src/embodied_tactile_control/include/embodied_tactile_control/latency_budget.hpp‎

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#pragma once
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#include <cstdint>
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namespace embodied_tactile_control
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{
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struct LatencyBudgetUs
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{
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uint32_t preprocess{410};
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uint32_t inference{650};
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uint32_t control{220};
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};
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} // namespace embodied_tactile_control

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