Heatmap-Guided Grasp Detection

• The paper presents a two-stage framework that generates semantic heatmaps followed by localized 6-DoF grasp regression, achieving competitive precision on constrained hardware.
• Heatmap-guided grasp detection is a method that transforms RGB-D sensory data into spatial probability maps to efficiently pinpoint optimal grasping locations.
• By integrating input downsampling, quantization, and hardware-aware model partitioning, the approach enables near real-time performance with minimized memory footprint on edge devices.

Heatmap-guided grasp detection refers to a family of methods in robotic perception and manipulation where visual or geometric cues are transformed into spatial “heatmaps”—typically image- or point-based probability or quality fields—that highlight the most promising grasp locations and parameters. These approaches are particularly prominent in deep learning–driven vision pipelines for 2D and 3D object grasping, and have recently been adapted for deployment on resource-constrained edge devices, making real-time execution feasible in embedded robotics environments (Bröcheler et al., 18 Jul 2025).

1. Framework Characteristics and Key Principles

Heatmap-guided grasp detection methods commonly partition the grasp synthesis problem into two sequential or parallel stages:

1. Heatmap Generation: An encoder–decoder neural network processes sensory input (such as an RGB-D image) to generate one or more dense heatmaps. Each heatmap provides per-pixel (in 2D) or per-point (in 3D) values indicating the suitability of local regions for grasping, often tied to attributes such as grasp width, orientation, or quality.
2. Anchor Sampling and Grasp Regression: Points or regions with high heatmap values are selected as “anchors.” For each anchor, a secondary feature extraction module (typically operating on a localized 3D neighborhood) regresses the full 6-DoF grasp parameters (position and orientation), yielding grasp hypotheses that can be evaluated for execution.

In the HGGD framework, for instance, the overall inference pipeline is structured around two networks:

• AnchorNet: Encodes the RGB-D input to produce semantic heatmaps for attributes like grasp width $H_w$, rotation $H_\theta$, and depth $H_d$;
• LocalNet: Combines anchor locations (sampled from heatmaps) with 3D point cloud features to predict the complete 6-DoF grasp $$g = \{x, y, z, \mathrm{roll}, \mathrm{pitch}, \mathrm{yaw}\}$$.

The core mathematical operation underlying heatmap extraction can be formalized as:

$H(i,j) = \sigma\left( (W * I)(i,j) + b \right)$

where $H$ is the heatmap, $I$ the input, $W$ a convolution kernel, $b$ bias, and $\sigma$ an activation.

2. Algorithmic Pipeline and Mathematical Formulation

The standard algorithmic stages in heatmap-guided grasp detection, as implemented in HGGD (Bröcheler et al., 18 Jul 2025), are:

a) Input Processing and Resolution Reduction

To address edge computing constraints, input RGB-D images are downsampled (e.g., from $640 \times 360$ to $320 \times 160$). This reduces memory bandwidth and enables on-chip execution, as the number of activations for convolutional layers is reduced by approximately 75%.

b) Semantic Feature Extraction (AnchorNet)

A residual convolutional backbone (ResNet34) encodes the input into a lower-dimensional feature map. Decoder layers then predict multiple heatmaps:

• $H_w$: grasp width candidates
• $H_\theta$: grasp orientation (possibly quantized/continuous)
• $H_d$: local grasp depth Each heatmap encodes spatial probabilities or regressed attribute values, with maxima indicating strong grasp hypotheses.

c) Anchor Proposal and 3D Correspondence

High-value points (“anchors”) are sampled from heatmaps, often with non-maximum suppression or thresholding. These 2D locations are mapped to the corresponding 3D points in the observed scene via backprojection using the camera depth channel.

d) Local Feature Extraction and Pose Regression (LocalNet)

For each anchor, a lightweight PointNet-style (Bröcheler et al., 18 Jul 2025) feature extractor processes the local 3D region to regress the complete 6-DoF grasp:

$g = f(\{H_w, H_\theta, H_d\}, p)$

where $f$ is the learned regression function and $p$ the 3D anchor.

3. Hardware-Aware Model Optimization

To enable real-time execution on low-power microcontrollers (such as the GAP9 RISC-V SoC), the HGGD framework introduces a series of hardware-specific optimizations:

- Input Dimensionality Reduction: As discussed, lowering the input image resolution directly constrains the memory footprint of feature maps and activations in convolutions.

- Model Partitioning: The model is segmented into four major stages—ResNet-MCU, AnchorNet-MCU, PointNet-MCU, LocalNet-MCU—to limit peak memory usage and enable pipelined (or multi-SoC) deployment. Each submodel runs sequentially, only holding its immediate working set in RAM.

- Quantisation: Model weights are converted from float32 to int8 using scaled quantisation, resulting in a 4× reduction in memory usage. This change is implemented as:

$w_\mathrm{quant} = \mathrm{round}(s \cdot w)$

with $s$ a learned scaling factor.

- Efficient Memory Layout: Data is auto-tiled and memory-aligned for fast DMA transfer due to limited or absent memory caching on the GAP9.

- On-Chip Accelerator Utilization: Convolution and batch operations are offloaded to a dedicated neural network accelerator (NE16), while RISC-V cores handle sparse or logic-heavy elements.

4. Experimental Findings and Performance Metrics

HGGD’s feasibility and efficiency were tested on the GraspNet-1Billion benchmark, with key metrics:

• Average Precision (AP): On “seen” and “similar” splits, HGGD-MCU delivered AP values close to state-of-the-art models running on workstation hardware even after downscaling and quantisation. This suggests effective preservation of detection quality under aggressive hardware constraints.
• Inference Latency: Full forward-pass latency (including all pipeline stages) averaged ~740 ms per frame on the GAP9 MCU. The primary bottleneck was LocalNet’s per-anchor PointNet operation, which is not efficiently parallelized on the available hardware.
• Memory Footprint: Shrinking input size and quantisation ensured that all sub-models’ working sets fit within the limited RAM of the MCU and the overall model within the 16MB flash memory.

5. Practical Implications for Autonomous Manipulation

Deploying heatmap-guided grasp detection on edge platforms demonstrates several practical benefits and trade-offs:

• Real-Time Feasibility: Although 0.74 s per frame falls short of high-speed pick-and-place applications, it is operationally sufficient for deliberate, careful grasping—suitable for low-velocity arms or fine tasks.
• Low-Power, Embedded Operation: The framework enables fully autonomous manipulation in form factors previously restricted by power or compute budgets, such as mobile robots and distributed sensor networks.
• Open-Source Validation: The model’s partitioning strategy introduces a robust design for modular deployment and paves the way for further optimization through parallel edge execution.

A plausible implication is that with further hardware advances and pipeline-optimized PointNet variants, heatmap-guided perception/grasper models can bridge the gap between perception accuracy and real-time autonomy in severely resource-limited environments.

6. Summary Table: HGGD Model and Hardware Characteristics

Component	Optimization	Role
AnchorNet	Downsampling, int8 quantization	Semantic feature/heatmap extraction
LocalNet	Model partition, int8 quantization	6-DoF grasp pose regression
Pipeline	Staged execution, auto-tiling	Memory and power efficiency
Hardware	RISC-V + NE16 accelerator	On-chip, real-time inference

7. Research Context and Comparison

The methodology of HGGD aligns with a broad class of heatmap-driven grasp synthesis approaches, wherein probabilities or qualities are predicted on a spatial grid (image or point cloud), and further local or global regressors convert these scores into robot-executable grasp configurations. This general paradigm underpins a range of approaches—such as multi-view pixel-level grasp prediction (Asif et al., 2018), prompt-driven joint segmentation/heatmap heads (Noh et al., 19 Sep 2024), and hierarchical heatmap propagation (Yang et al., 30 Oct 2024)—that balance detection accuracy, contextual reasoning, and computational efficiency.

Unlike methods that require workstation-scale resources, HGGD demonstrates—with explicit microcontroller targeting, input scaling, and staged execution—the practical viability of heatmap-guided grasp detection in real-time, embedded, and autonomous manipulation scenarios (Bröcheler et al., 18 Jul 2025).